Research ProposalPDF Available

Title of Research Proposal Towards Smart Cities: A Move for Efficient Energy Management from a Home to Cities Exploiting Clouds Signature of Student

February 2019

DOI:10.13140/RG.2.2.18552.70404

Authors:

COMSATS University Islamabad

The reliable, efficient, sustainable and optimal management of city resources to facilitate the inhabitants define the Smart City (SC). A SC has many management sectors; however, the power sector is the backbone, which has a complex and expensive structure. The integration of the conventional power grid with Information and Communication Technologies (ICTs) defines the Smart Grid (SG). Two-way communication between supply and demand sides provides an opportunity to optimize power production and consumption. In this synopsis, Demand Side Management (DSM) is proposed for the residential sector. The residential sector is divided into islanded Smart Homes (SHs) and smart communities. In SHs, the appliances are scheduled to shift the load from on-peak to off-peak hours to reduce the energy cost and mitigate the load peak demand. In this synopsis, four scheduling algorithms are proposed for a SH’s appliances. The cost efficient energy consumption using the algorithms shall be analyzed by implementing with three pricing schemes; Critical Peak Pricing (CPP), Day Ahead- Real Time Pricing (DA-RTP) and Inclined Block Rates (IBR) with three Operation Time Intervals (OTIs); 10, 30 and 60 minutes for a SH. The habitants of a set of SHs in a region or building in a city share the things on common grounds, which define the community. When SHs of a region schedule their load from on-peak to off-peak hours, the load peak is generated in off-peak hours. In the synopsis, four cloud-fog based system models are proposed for communities to optimize energy consumption with time efficiency. The Response Time (RT), Processing Time (PT) and computing cost of the system are optimized with Service Broker Policies (SBPs) and load balancing algorithms (managing the load of requests and tasks) to balance the load of physical and virtual resources for the fog. SBPs route the requests on potential data center and load balancers (heuristic techniques) manage the load of requests on virtual resources efficiently. The quality of resource utilization effects the RT, PT and computing cost. Cloud-fog based models for realistic environments, in which Fog-as-a-Power-Economy-Sharing and Fog based Energy Management as a Service (FEMaaS) for prosumers are proposed for communities. The cost efficient energy consumption and efficient computing cost will validate the feasibility of proposed system. The proposed research will strengthen the concept of SC with cost efficient energy management in SG.

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COMSATS University Islamabad, Islamabad Campus

Synopsis For the Degree of M.S/MPhil. 

XPhD.

PART-1

Name of Student Rasool Bukhsh

Department Computer Science

Registration No.

FA15-PCS-006 Date of Thesis Registration

Name of

(i) Research Supervisor

(ii) Co-Supervisor

(i) Dr. Nadeem Javaid

(ii) Dr. Majid Iqbal Khan

Research Area Energy Optimization in Smart Grid

Members of Supervisory Committee

1 Dr. Nadeem Javaid

2 Dr. Majid Iqbal

3 Professor Dr. Sohail Asghar

4 Dr. Muhammad Manzoor Ilahi Tamimy

Title of Research Proposal Towards Smart Cities: A Move for Efﬁcient Energy Management

from a Home to Cities Exploiting Clouds

Signature of Student:

Summary of the Research

The reliable, efﬁcient, sustainable and optimal management of city resources to facilitate the in-

habitants deﬁne the Smart City (SC). A SC has many management sectors; however, the power

sector is the backbone, which has a complex and expensive structure. The integration of the con-

ventional power grid with Information and Communication Technologies (ICTs) deﬁnes the Smart

Grid (SG). Two-way communication between supply and demand sides provides an opportunity to

optimize power production and consumption. In this synopsis, Demand Side Management (DSM)

is proposed for the residential sector. The residential sector is divided into islanded Smart Homes

(SHs) and smart communities. In SHs, the appliances are scheduled to shift the load from on-peak

to off-peak hours to reduce the energy cost and mitigate the load peak demand. In this synopsis,

four scheduling algorithms are proposed for a SH’s appliances. The cost efﬁcient energy con-

sumption using the algorithms shall be analyzed by implementing with three pricing schemes;

Critical Peak Pricing (CPP), Day Ahead- Real Time Pricing (DA-RTP) and Inclined Block Rates

(IBR) with three Operation Time Intervals (OTIs); 10, 30 and 60 minutes for a SH. The habitants

of a set of SHs in a region or building in a city share the things on common grounds, which deﬁne

the community. When SHs of a region schedule their load from on-peak to off-peak hours, the

load peak is generated in off-peak hours. In the synopsis, four cloud-fog based system models are

proposed for communities to optimize energy consumption with time efﬁciency. The Response

Time (RT), Processing Time (PT) and computing cost of the system are optimized with Service

Broker Policies (SBPs) and load balancing algorithms (managing the load of requests and tasks)

to balance the load of physical and virtual resources for the fog. SBPs route the requests on po-

tential data center and load balancers (heuristic techniques) manage the load of requests on virtual

resources efﬁciently. The quality of resource utilization effects the RT, PT and computing cost.

Cloud-fog based models for realistic environments, in which Fog-as-a-Power-Economy-Sharing

and Fog based Energy Management as a Service (FEMaaS) for prosumers are proposed for com-

munities. The cost efﬁcient energy consumption and efﬁcient computing cost will validate the

feasibility of proposed system. The proposed research will strengthen the concept of SC with cost

efﬁcient energy management in SG.

1 Introduction

Electricity generation and distribution are expensive and complex processes. In the conven-

tional power grid, power is produced according to demand and transmitted to end-users. The

rise in power demand causes high production. The power plants run on fossil fuels, which pol-

lutes the environment due to the high emssion of carbon [1]. Unfortunately, more than 65%

of the total produced electricity is wasted during the processes of production, transmission and

distribution [2]. The 80% of the world’s population has access to electricity and technologi-

cal developments have rapidly increased the electrical and electronic gadgets from personal to

corporate usage [3]. Moreover, power consumption behavior of the masses deﬁnes the on-peak-

load and off-peak-load on the supply side. The power plants run the generators on fossil fuels,

which emit the carbon-dioxide. The huge amount of carbon dioxide is emitted during on-peak

time due to the functioning of additional generators to fulﬁll the load demand. The efﬁcient

energy management optimizes energy consumption on the demand side to avoid the on-peak-

load as well as to minimize carbon gas emission. The high concentration of the gas pollutes

the environment and has greenhouse effects. A variety of optimization solutions for demand

side energy management have been proposed. However, an efﬁcient mechanism for two way

communication between supply and demand sides guarantees the effective energy consumption

management. The integration of Information and Communication Technology (ICT), to provide

communication platform, with the traditional power grid makes the Smart Grid (SG).

The energy management is required in various scenarios, i.e., Smart Homes (SHs), smart

communities, integration of renewable energy with utility, energy trading and incentives for

prosumers. A SH optimizes power consumption by scheduling the load; however, for com-

munities, energy is managed in distributive or centralized pattern. In this synopsis, scheduling

algorithms are proposed to optimized energy consumption of home appliances and cloud-fog

based semi-centralized system models are proposed for rigid and ﬂexible communities. The

various components used for energy management in SHs and smart communities are discussed

in following subsections.

1.1 The SG

The National Institute of Standards and Technology (NIST) has deﬁned the SG as the integration

of power infrastructure with communication and computing services. The two way communi-

cation and energy controlling enable the new applications and functionalities for businesses [4].

The intelligent integration of electricity network and actions of end-users incorporating the lat-

est smart technologies ensures a sustainable, economic and secure supply of electricity is called

SG; deﬁned by European technology platform (European Commission 2006). The Smart Me-

ters (SMs), smart appliances, Renewable Energy Sources (RESs) and Energy Storage Systems

(ESSs) are used on the demand side for intelligent energy usage. However, power production,

transmissions and distribution are the vital operations performed with advanced ICTs in SG.

The ICTs enable the SG to be operated within time frames using control command deﬁned by

international standards, e.g., IEEE-1547 (an IEEE standard deﬁned for control and management

of distributed energy sources). The SG provides simultaneous and smart interaction of power

operators and consumers to attain the objectives of safe, efﬁcient and reliable power system [5].

The global electricity demand has increased in recent years and it is expected to increase

exponentially till 2050 [6]. The scientists propose the integration of ICTs and RESs with tra-

ditional power grids to control the greenhouse effects due to carbon emission. The integration

of RESs increase the complexity of the power system and has become challenging to stabilize

the distributed power generation at large scale [7]. The autonomous smart decisions of SG

promise the integration of RESs, ESSs and utility with fault-detection, self-healing, monitoring

and controlling to balance between supply and demand sides. In SG, energy management is di-

vided into Supply Side Management (SSM) and Demand Side Management (DSM). The energy

production, transmission and distribution are performed on the supply side; however, planning,

monitoring, scheduling of loads are performed on the demand side.

1.2 The DSM

The SG is scalable and can integrate RESs, distributed grids, ESSs and SHs on demand side.

The demand side consists of industrial, commercial and residential users for Demand Response

(DR) activities [8]. The DSM educates the consumers to reduce power demand or schedule

their energy demand to avoid peak-load. The consumers are encouraged to optimize their power

consumption by providing them ﬁnancial incentives [9]. The stability of the grid is attained

only with balanced demand and supply. On the peak-load demand, additional generators are

turned on to fulﬁll the demand. The DSM is comprised of various strategies to convince the

consumers to reduce the energy consumption during peak-load demand, e.g., peak clipping,

shifting load, valley ﬁlling, energy conservation [10]. In peak clipping strategy, the consumer

reduces the power consumption by turning off unnecessary or low priority appliances via direct

load control. In load shifting techniques, incentive based schemes are proposed to encourage

the users to shift load from on-peak to off-peak time. Here, the total load demand of consumer

remains unchanged. In the valley ﬁlling strategy, the load builds during peak time. An effort

to reduce energy consumption by modiﬁcation of services is known as energy conservation

strategy, e.g., driving less.

1.3 DSM in Residential Sector

The growing electricity demand compels to produce more energy, which causes high carbon

emission. The Energy Management Systems (EMS) for optimized and efﬁcient energy con-

sumption on demand side minimize the energy demand. To analyze the demand side, it is

further divided into industrial, commercial and residential sectors. The commercial buildings

like hotels, hospitals, ofﬁces and stores are reluctant to participate in energy optimization due to

their business and proﬁts [11]. Industries are also reluctant to schedule their energy due to their

huge proﬁt [12]. The residential sector has more elastic behavior of power consumption as com-

pared to industrial and commercial sectors [8], which provides a high opportunity to optimize

power consumption [1], [13]-[14]. The information shared between the supply side and resi-

dential sector encourages the consumers (on the demand side) to use cost efﬁcient micro-power

generators to avoid expensive utility energy. The variety of RESs and ESSs are widely accepted

in the residential sector to fulﬁll cost efﬁcient energy demands [15]. When homes shift from

utility to RESs or ESSs for cost efﬁcient energy consumption, it off-loads the utility to shave the

peaks of high load demand, which minimize power production and carbon emission. The SHs

with smart appliances schedule the operation of appliances to shift the load for cost efﬁciency

[16]-[17].

Load optimization techniques are applied to schedule the appliances in SHs in the residential

sector for cost efﬁcient power consumption. However, consumer’s satisfaction is compromised

due to the shifting of appliances’ operations from the desired time. The RESs based Micro Grids

(MGs) provide uninterrupted and cost efﬁcient power supply; however, RES based MGs are ex-

pensive and difﬁcult to maintain. Moreover, the intermittent nature of the sources makes the

system complex [18]. ESS provides an alternative and effective solution to solve the aforemen-

tioned problem. Battery based ESS (BESS) is resilient and affordable with lesser maintenance

as compared to RES. The BESS is a popular power backup solution for residential consumers

[19]. The energy storage companies have been developing scenario based power solutions using

the storage systems [13]. These power solutions have high ﬂexibility, reliability and availability.

To avoid expensive energy from the utility during on-peak hours, the storage systems provide

the promising cost efﬁcient solution [14], [17]. The demand side management with energy stor-

age system reduces the load on the utility and also minimizes the cost of consumption [20]-[21].

The RESs based energy solution is more beneﬁcial for multiple homes (e.g., community); how-

ever, for islanded SHs, BESSs are popular. So, in SG the residential sector is further divided into

islanded SHs and the communities. The efﬁcient energy management from SHs to communities

eventually leads to Smart City (SC) energy management.

1.3.1 Islanded SH

The presence of conventional and SHs in a residential area consume the energy at utility deﬁned

pricing. The utility broadcasts the tariff information and conventional homes consume energy

at deﬁned rates; however, SHs optimize their power consumption to reduce energy consumption

cost. The SHs use the features of ICTs to automate and control the smart appliances [22]. The

Home Energy Management Controller (HEMC) manages the appliances to consume energy ef-

ﬁciently. The HEMC is installed with intelligent programs, which ﬁnd optimal, economical and

reliable energy optimization solutions with high user comfort at reduced cost and curtailment

of peak-load. A variety of mathematical and heuristic load scheduling techniques for various

scenarios; considering single and multiple homes, have been proposed [1], [14]. The use of

RESs or ESSs or both in islanded SHs also reduce the peak-load on the supply side and energy

consumption cost. The system models for cost efﬁciency with load scheduling for single and

multiple homes in the residential area have been proposed to analyze the load peaks reduction

and consumers’ cost minimization [23]. The load is optimized by shifting from on-peak to off-

peak time. If all the homes in the area shift their load from on-peak to off-peak time then the

peak will generate in off-peak time. This communal optimization is an inefﬁcient solution.

1.3.2 Community

The load optimization with respect to energy tariff is an efﬁcient solution when neighboring

SHs have a different solution in a community. The optimization of energy consumption in

consideration of whole community needs a centralized mechanism of energy optimization; e.g.,

cloud and fog based energy management [24], [25]. The development of ICTs provides the

platform for the Internet of Thing (IoT), which led to cloud and fog based systems for distributed

and centralized environment. The cloud has the huge computing resources as compared to

fog; however, cloud based system suffer from latency issue due to mega infrastructure [26].

The fog based energy consumption optimization is proposed to overcome the latency issues of

cloud based system [27]. The centralized energy management solutions are proposed depending

on the category of the community. The number of SHs in region or building, which cannot

be expended with additional SHs in future is the rigid community; however, the expandable

region with additional SHs in (near) future is known as ﬂexible community. The system model

with the computational resource, resource optimization, computing cost, energy consumption

optimization and time-efﬁciency are key challenges for rigid and ﬂexible communities.

1.4 The Cloud Computing

Cloud computing, referred as “the cloud”, delivers the services on demand on pay-as-you-use

basses. The NIST deﬁnes the cloud as, a model for enabling convenient, on-demand network

access to a shared pool of conﬁgurable computing resources (e.g., networks, servers, storage, ap-

plications, and services) that can be rapidly provisioned and released with minimal management

effort or service provider interaction [28]. However, researchers have redeﬁned cloud comput-

ing according to their use of services over the Internet. The IBM has deﬁned the cloud as, the

computing service over the Internet on pay-as-you-use bases with three basic services; Software-

as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) [29].

The resources of cloud computing are scalable to meet the demand. The services are metered

to calculate payments for use of services. The services are, usually, self-service for end-users

to customize according to their demands. In SaaS, the softwares run on distance computers (the

cloud), which are owned by a company to facilitate their users. The end-users’ machines are

connected with the cloud via the Internet for the software services. It gives the opportunity to

access the service from anywhere and anytime. Data is safe, if the user’s computer crashes, the

resources are automatically scaled according to demand or use. An instant business startup and

many more beneﬁts are there for SaaS on the cloud. The user does not need to have complete

hardware with software computing machine to manage complete life-cycle of building applica-

tion. All the service can be accessed via the Internet from the cloud of PaaS. The user does not

need to maintain personal computing machine and there is no risk of system crash. The cor-

porate companies can access the cloud for infrastructures like servers, networking, storage and

other computing resources via IaaS. IaaS provides ﬂexible, scalable and on-demand resources

without maintaining and investing in personal hardware resources.

The IBM also deﬁnes the types of the cloud according to their use and kinds of users.

For example, the public cloud is owned by the companies or government agencies to facilitate

the public to use the services (e.g., SaaS, PaaS, IaaS) without investing expensive personal

computing machines. The private cloud is owned by an organization to use solely for own use.

The physical systems are usually placed in the premises of the company to manage; however,

maintained by the third party or internally. These systems have company speciﬁc services only

and managed with sophisticated data security. The hybrid cloud is a combination of public and

private clouds. The companies access the services of the public cloud to manage their own

services.

The cloud has huge resources and provides access to heterogeneous service for a variety of

requests from the end-users via the Internet. The long physical distance between cloud comput-

ing resources and the users is resolved with routers and switches. However, a number of hops

between the user and the cloud as well as the load of tasks performed on the cloud compromise

the Response Time (RT) [30]. Moreover, a common pool of data of a variety of users makes it

unsecured [31].

1.5 The Fog Computing

The limitations of cloud based systems due to the distance between physical computing envi-

ronment and end-users as well as due to a huge common pool for data storage for a variety of

end-users can be solved with the fog computing system. The Cisco deﬁned the fog computing as

the extension of cloud computing from the core to the edge of network [32]. The fog resources

are placed at the edge of the network and close to the end-users. The end-users are usually

directly connected with the fog for high computation with minimum RT. The fog has limited

computing resources as compared to the cloud. Multi-layered systems are proposed in [33], if

more resources are required to the fog. The end-users’ layer is connected with the fog and the

fog is connected with the cloud to access the computing resources. The RT for the end-users’

requests is signiﬁcantly reduced with the fog as compared to the cloud based system. Moreover,

performance bottleneck is created for the fog when a huge load of tasks or requests reach out

from the end-users’ to process due to limited resources. However, an efﬁcient resource utiliz-

ing techniques are required to solve the aforementioned issue. The following basic components

are important to manipulate for the enhancement of the performance of the fog and the cloud

resources.

1.5.1 Communication Network

In SHs, home area network is designed for communication of smart appliances with centralized

EMC or among the appliance themselves. The communication helps to optimize power con-

sumption with respect to the information shared and architecture design with control programs

implemented in the SH. The network supports wired and wireless technologies. The wireless

media like Bluetooth, WiFi, WiMAX, etc. are used in the network [34]. A control program with

user interface links the SM, appliances and the consumers. The high bandwidth Internet and

proliferation of the IoT have accelerated the deployment of home area network with static and

mobile controlling facility. The IoT devices also encourage to design a network for distributed

and centralized environment for energy management using the cloud and fog based systems. In

fog based systems, the end-users are directly connected with the fog using long range wireless

like ZigBee as well as wired technologies for efﬁcient RT [35]-[36].

1.5.2 Virtual Machine

Virtual Machines (VMs) are softwares that mimic the physical machines. Unlike simulator that

mimics of how a physical machine works, the VM extends the utility by serving and supporting

the resources [37]. In fog and the cloud, VMs share the computing resources like storage and

processing of CPU [38]. Each VM acts as an independent resource while residing in the same

machine. In fog and the cloud, data centers have physical components for huge storage and

processing units, where VMs are created to share these huge resources. The VMs have sizes

and are created or killed according to the static or dynamic scenarios. The performances of the

fog and the cloud are affected by the size and number of VMs created. Moreover, the load of

requests or tasks are assigned to VMs in the data center; however, inefﬁcient load assigning

compromise the resource utilization. Meta-heuristic techniques are used to balance a load of

tasks on the VMs in the data center to enhance the performance.

1.5.3 Bio-inspired Heuristic Techniques

There are mathematical, logical and nature inspired solutions for optimization. The meta-

heuristic solutions for optimization inspired by nature are efﬁcient and effective. These are

inspired from Physics, Chemistry, Biology and computational psychology, etc.; however, bio-

inspired optimizing techniques are more adopted by the researchers as compared to rest of the

techniques [39]. Bio-inspired heuristic techniques are categorized into ﬁve categories based

on; evolutionary process, the behavior of collective swarm, ecological processes, human intelli-

gence and the physical phenomenon [40]. In SG, the algorithms inspired from these categories

are used for optimized power consumption on demand side. In islanded SHs, heuristic tech-

niques are used to optimize the power consumption of the appliances by shifting load from

on-peak to off-peak time. In fog and the cloud computing, the balanced load of tasks or requests

are allocated on VMs in the data centers.

1.5.4 Service Broker Policy

The Service Broker Policies (SBPs) are constraints used to route the tasks or requests on po-

tential data centers of the cloud and fog. The potential data center is selected on rules deﬁned

for efﬁcient RT. These rules consider the constraints like a nearest data center, load trafﬁc on

the network, a load of tasks or requests on the data center, shortest RT, load and RT prediction

and proximity. The SBPs are also deﬁned in consideration of data center architecture, design

(distributed/centralized), data center energy consumption, static and dynamic load trafﬁc and

routing mechanism, etc. [41]-[42]. The SBPs and load balancing algorithms manage the tasks

or requests onto the computing resources to overcome the latency issues. Moreover, efﬁcient

resource utilization also reduces the computing cost.

1.5.5 Prosumers: Integration of Renewable Energy with Utility

National policies are set by many countries to generate and integrate green energy with the

utilities. The European Union has set the goal to reduce greenhouse gases by generating 35% of

power from RESs [43]. However, 14% of world’s power is being supplied by RESs [43]. The

deployment of MGs with RESs and ESSs can fulﬁll energy demand and can reduce emission of

CO2[44]. The information and communication technologies provide efﬁcient power production

and consumption with autonomous, sustainable, scalable and self-healing characteristics to form

SG [45]. The SGs around the globe require efﬁcient energy management in such a way that

renewable energy producers are encouraged to participate for their maximum revenue. For

this purpose, small and large scale renewable power generators should be conﬁgurable in the

existing SG systems. Around 179 countries have set the target to fulﬁll part of power demand

from green energy sources; by 2020 Germany has set the target to produce 35% energy from

RESs, U.S states like California has set 33% and the Republic of Korea has set 10% [46].

The governments have launched subsidized projects for renewable power usage as social

welfare without considering the willingness of the public to pay [47]. These projects overburden

the expenditure for governments. As a result, Spain stopped subsidizing on PVs in 2012 and

in European countries such schemes are partly reduced [48]. Therefore, the trading strategies

can encourage large and small scale renewable energy producers to participate in an integrated

power grid system. It reduces carbon emission while fulﬁlling energy demand.

The integration of Distributed Energy Resources (DERs) is challenging due to various fac-

tors like intermittency of RESs, uncertain energy demand, scalability of the system and number

of participants in the system, etc. [49]-[50]. In the literature, a variety of solutions have been

proposed to integrate renewable and stored energy with the power grid. Moreover, a variety

of bidirectional dispatch network designs for power ﬂow have been investigated for distributed

generators [51]-[53]. The conventional power distribution system has the limitation for dis-

patching Distribution Generation (DG), which makes the unmanaged DG output accepted pas-

sively for power grids and local demand load. In the SG, renewable energy is managed by

clustering DG installations to form the Virtual Power Plant (VPP) [54], which is operated by

the centralized control unit. The well-managed cluster of DERs can maximize their proﬁt by

considering their types, geographic locations, energy efﬁciencies and their capacities [55]. The

energy management techniques with DR on demand side considering conventional and VPP

promote market efﬁciency and reliability, which are currently operated in independent system

operators, regional transmission organization [56] and distribution system operators [57]. Simi-

larly, MGs optimizes energy states in islanded and grid-connected mode [58], [59]. The efﬁcient

management for unmanaged DG, VPP, DR and MGs is expensive to implement. Moreover, the

complexity increases with business and operation models by the deployment of more DERs.

To maximize the incentive for distributed energy producers net metering provides economic

beneﬁts even at small scale [60]. It allows prosumers to participate in the energy market to trade

surplus energy in this power market by choosing a Retail Energy Provider (REP), which offers

buy-back programs. However, there are few, which buy-back the energy [61]. The residential

prosumers produce a small amount of surplus energy and the residents face the difﬁculty to

have sufﬁcient choices. To tackle such issues, authors have proposed a variety of centralized

solutions like cloud computing based energy management system.

The cloud based systems have latency issue due to the processing of a load of tasks and

the infrastructure designs. The end-users are physically at distance from the actual processing

machine, which also increase the RT. In this paper, the Fog based Energy Management-as-a-

Service (FEMaaS) for smart communities is proposed. The residents of the communities are

equipped with RESs and ESSs to consume cheaper energy. The FEMaaS forms the virtual

REPs out of these distributed energy generators, which are integrated with existing REPs. The

resident of a smart community who produces and also consumes the energy is called prosumer.

The FEMaaS provides intermediate services between the utility and the prosumer for power

generation, trading and consumption with the optimized choice. The FEMaaS cooperates with

different components of the system through Linear Programming (LP) model to: 1) minimize

global cost or maximize the incentives to encourage prosumers and companies to equip RESs

and 2) enhance the integration of renewable energy by following the commitments. The commit-

ments are contracts designed for FEMaaS and utility, on which both are agreed. Such contracts

already exist between utilities and REPs [62].

2 Related Work

In this section, literature for energy management in residential sector has been discussed for

islanded SHs and for communities;

2.1 Islanded SHs: Energy Optimization for Cost Reduction (RESs, ESSs and Util-

ity)

The authors [63] introduce the management framework for Electrical Vehicle (EV) charging and

discharging for efﬁcient utilization of PhotoVoltaic (PV) throughput. A coordination between

home energy management and grid management systems is established by exchanging the infor-

mation. The charging-discharging plan of EV assists the Home EMS (HEMS) to reduce the cost

and curtailment of PV without compromising the EV energy demand for driving. The proposed

framework is evaluated on the simulated model and the results advocate the efﬁciency of the

framework in terms of cost reduction for the SH and PV curtailment. Similarly, the authors in

[64] propose a stochastic based dynamic programming framework energy management of a SH

using EV to overcome intermittency of RESs. Battery model for EV, the probabilistic behavior

of EV drive and operating modes among EVs, EVs-to-grid, grid-to -EVs, SH’s demand and

utility rates are the elements of the framework. The simulation results show the optimized cost

for SH and operational cost between EV and SG [65].

The appliances scheduling on the basis of experience for operations of SH’s appliances is

an effective technique. The authors of [66] propose a Quality of Experience (QoE) based HEM

for a SH. The appliances of a SH are scheduled using the Cost Saving Appliance Scheduler

considering the QoE; (Q-CSAS) algorithm with Time Of Use (TOU) pricing, which suffers the

appliances. Where there is surplus energy by considering the QoE, the appliances are scheduled

using Renewable Source Power Allocation (Q-RSPA). The simulations are performed for 1000

SHs and results show that the proposed algorithms are 22% cost efﬁcient without RESs and

are 30% of cost is reduced with RESs installation. A distributed HEMS is proposed by [67].

The SHs schedule the appliances, consume energy from storage and the utility. Local energy

optimization is achieved by scheduling the devices according to day-ahead pricing. The pricing

structure is deﬁned on generation and consumption; however, the distributed control method is

proposed for energy efﬁciency and cost optimization for every SH.

An EMC is proposed by [68] that uses three scheduling algorithms: Ant Colony Optimiza-

tion (ACO), Binary Particle Swarm Optimization (BPSO) and Genetic Algorithm (GA). The

simulations are performed for three categories of SH’s appliances: ﬁxed, interruptible and non-

interruptible. The algorithms schedule the appliance to avoid peak pricing hours using TOU and

IBR pricing signals. The results show that the GA reduce more Peak to Average Ration (PAR)

and electricity bill with maximum user comfort than ACO and BPSO. The ACO and BPSO have

high time and space complexity.

A multi-objective Home Energy Management (HEM) is proposed by [69]. The objective of

HEM is to reduce the consumer’s maximum cost by managing the shiftable and nonshiftable

appliances of a SH. The sequential system model averts the peak-load in MG by scheduling

shiftable appliance in SH and managing plug-in EVs, PV panels and small scale storage system.

The linear techniques are used to prevent non-linear nature of the proposed system model. The

simulation results with three scenarios advocate the load and cost efﬁciency of the proposed

system model. Another multi-objective system model is proposed by [70]. The system is a

combination of Multi-Objective Dynamic Economic and Emission Dispatch (MODEED) and

DSM to analyze the beneﬁciary effects of DSM on SSM. In a test system of six thermal units,

the objective of MODEED is reduced by Multi-Objective Particle Swarm Optimization (PSO);

(MOPSO) and the results are compared with Non-dominated Sorting GA (NSGA). The simula-

tion results show that the proposed system model has beneﬁts for DSM and SSM. However, the

thermal power generation system is harmful to the environment while the same system can be

tested for RESs based MGs [71].

Converting a conventional home into SH helps the DSM to optimize the power consumption.

The authors of [72] propose a wireless plugins for conventional homes to control the operation

of appliances. The SM receives the two pricing signals: day ahead and real-time, from the

power companies. The SM controls the appliances using plugins by shifting the operation time

of appliances with the proposed algorithm. The appliances are shifted in less tariff hours, which

reduces the power consumption bill. The simulation results show that SM controller and the

wireless plugins reduce 8% power consumption cost in a day. The authors in [73] propose a

HEMS architecture. The control algorithm manages battery storage and temperature of appli-

ances. The proposed storage system stores the energy during off-peak hours, which is utilized

during on-peak hours. The temperature of thermal sensitive appliances is managed during on-

peak hours. The algorithm maintains the maximum energy in the storage system during the day.

The simulation results show that the proposed HEMS architecture with the algorithm reduces

20% cost for a day. The user satisfaction remained maximum.

Every SH has its own behavior of power consumption, which depends on the residents. The

optimization of multiple SHs with their unique behavior of power consumption is a challenging

task. The authors [1] consider multiple SHs with multiple appliances of different power rating

and propose a DSM scheme to reduce electricity bills and PAR. Cuckoo Search Algorithm

(CSA) and strawberry algorithm are used to schedule the appliances of SHs. Every SH has

own energy storage system, which is charged from the utility during the off-peak hour and used

during the on-peak hour. The surplus energy of the storage system is sold back to the utility to

reduce electricity cost. The simulations are performed for the proposed system model with the

schemes in three scenarios. The results show that CSA is more cost efﬁcient than strawberry

algorithm.

The storage system is not suitable for some scenarios due to its life and the constraints like

heating and maintenance. The efﬁciency of RESs are dependent on geographical and environ-

mental conditions. The authors [74] propose a cost effective solution for the SH, which has

its own PV based MG, which is connected to the utility. A genetic wind driven optimization

algorithm is proposed to schedule the shiftable and non-shiftable appliance. The simulations are

performed with two pricing signals: real time pricing and IBR. The proposed scheme efﬁciently

shifts the appliances from off-peak hours to on-peak hours and reduce the maximum cost. The

simulation results show that the proposed algorithm reduces PAR and cost more efﬁciently than

GA and BPSO.

Three case studies are discussed in [75] to study the energy saving for Singapore house-

holds with smart technologies. The authors suggested to adopt smart technologies for SHs to

save maximum energy. The analysis shows that the awareness of environmental pollution, se-

curity and comfort convince an individual to alter energy consumption behavior. The immature

technology and poor design discourage the consumers to adopt smart technology. Artiﬁcial

Intelligence (AI) based technologies that are integrated with public service and the utility sec-

tors, convince the consumers to use smart technologies. The residents of buildings are provided

with SMs in Singapore to detect power consumption behavior and these take action according

to the predeﬁned program. The SM controls the appliances without making the consumer an

active operator. The consumer is notiﬁed on mobile gadgets and on automated house console

(which were provided to them). The analysis shows that SMs provided to the consumers are

in immature phases, which discourage the consumers to adopt the proposed smart system. The

ﬁndings of this research shall be helpful for projects like Singapore Smart Nation (SSN) [76].

Researchers are hopeful with such leading projects to improve the technology and SHs in near

future in Singapore.

The DSM requests for more power during peak demand hour and the SSM has to increase

power production, accordingly. The increased power production adds carbon dioxide in the

environment, which pollutes it. Moreover, high production increases the tariff rates. The au-

thors of [77], propose a model to determine the peak demands for limited number of appliances.

Four scenarios are simulated to investigate the peak demand with real time pricing. The pro-

posed model shaves the peak demand and reduces the energy cost. The performance evaluation

protocol reveals that peak demand is aggressively increasing for unlimited (huge number of)

appliances. The authors in [78] propose RES based energy model for HEMS. The size of RESs

is analyzed using GA for charging and discharging of battery storage. The study shows that the

proposed system contributes to load demand.

A multi-objective energy management model for the building is proposed in [79]. The

building has RESs based MG for cheaper energy. The model is implemented with TOU pricing

scheme. The simulation results validate the effectiveness and adaptability of the proposed en-

ergy management model. The use of sparse energy load resolves the user discomfort problem.

The authors in [80] propose distributed load shift for user comfort with cost and PAR optimiza-

tion. A centralized coordination for distributed load on demand side is maintained by employing

the Newton method. The simulation results show the Newton method is efﬁcient as compared

to Nash equilibrium method.

The end-users in SG are educated to optimize their load consumption; however, for last few

years, the insightful researchers are proposing autonomous DSM programs [81]. The purpose

of the DSM is to autonomously optimize the load consumption on the demand side. In [82], the

authors propose the cloud based EVs charging and discharging system for SG. Priority assign-

ment algorithm optimizes and reduces the waiting time for EVs on public supply stations. The

proposed system manages SG operations and maintains the communication between the cloud

and SG. The simulation results validate the usefulness of the proposed approach for charging of

EV during on-peak hours, which improve the SG stability.

In the literature, various scenarios, system model and algorithms have been proposed to op-

timize the energy consumption to reduce the cost and user discomfort. The DSM is responsible

for energy production, energy cost and user comfort [83]. MGs with RESs and storage systems

are expensive and complex. Every SH is not necessarily supposed to afford the MG; however,

appliances are scheduled to avoid costly tariff hours. A SH is assumed to be connected with

utility, which has eight appliances. The appliances are scheduled to reduce maximum energy

consumption cost. Four hybrid scheduling algorithms are proposed. The effectiveness of pro-

posed algorithms is analyzed by simulating with three types of pricing signals and three different

Operation Time Intervals (OTIs).

2.2 Community: Infrastructures, Energy and Resource Management (Cloud, Fog)

The cloud has huge computing resources to deal with huge data and tasks from various lo-

cations. The cloud based systems provide centralized service for distributed and centralized

environments. The state-of-the-art cloud, fog, cloud-fog based systems and efﬁcient resource

allocation techniques have been discussed in followings sub-sections.

2.2.1 Cloud based Distributed Systems

In [84], the authors proposed an architecture that is decentralized cloud based architecture.

The authors aimed to schedule the requests sent by the consumers side in a real-time envi-

ronment. For this purpose, they have performed experiments for real-time electric load data of

Toronto city in Canada. However, a pricing model is proposed for energy load optimization dur-

ing the on-peak hours by maintaining stability of MG. A new SBP is proposed for data center

selection in [85]. The authors used master fuzzy context for provisional fuzzy logic. The fuzzy

rules are designed for Optimized RT (ORT) and data center priority.

The authors of [86], propose the stochastic model for consumers to schedule the energy load

of appliances using the cloud based DSM in SG. The model creates small energy hubs for users

and shifts the load from on-peak to off-peak hours using Monte Carlo method. The proposed

model reduces the PAR and the cost. However, authors of [87] analyze the Distributed DR

(DDR) and Cloud based DR (CDR). The comparative analysis of simulation results shows that

CDR performs better than DDR with high scalability, reliability of power and stable commu-

nication network. Unlike CDR the DDR has unreliable channels, which are prone to lose the

messages. The simulation results show that the proposed CDR reduces more cost and PAR as

compare to DDR.

2.2.2 Cloud based Centralized Systems

A cloud based model for direct current nano-grid is proposed for next generation smart cities in

[88]. Low voltage smart appliances are controlled for energy and cost efﬁciency using cloud-

based energy management. The model is proposed with the uninterrupted power supply to

reduce maximum cost. For experimental analysis, a group of buildings from the SC is taken,

which are controlled by generating alerts from the data centers on high power consumption.

These data centers are connected with a centralized cloud infrastructure to control the energy

consumption of SC buildings. The simulation results show that the proposed scheme has high

satisfaction ratio, curred delay and reduced demand supply gap. The authors in [89], propose the

analysis for new trends in next generation cloud computing. The cloud infrastructure is modiﬁed

into multi-tier architecture and applications are also modiﬁed accordingly. The multiple infras-

tructure providers and distributed computing have resulted in new cloud infrastructure. The

authors analyze that emerging cloud architecture will improve the connection between people

and devices. It will provide self-learned cloud based system.

The cloud is a cost efﬁcient platform for the delay and data security immune applications.

The authors of [90] propose a cloud-to-edge system in which cloud services migrate to the edge.

The existing cloud based system is connected with the edge system using message-oriented

middleware. The middle-ware uses the instant message protocol for efﬁcient data sharing from

the cloud to the edge; however, data security is the challenge. The authors address the data

security issues and overcome the data security, authenticity and integrity in the cloud-to-edge

system. The experimental results validates the data security when services move from cloud to

the edge without compromising the performance. The security wall is not introduced for time

efﬁciency in inter modules. However, it is highly recommended to introduce a security wall for

the program to program, people to program and people to people for secure communication.

The challenge is to overcome the latency due to the security wall.

The authors in [91] propose a model of bi-level optimization to schedule power consumption

of local appliances and amount of power production. Adequate constraints on optimization

vector reduce the cost of power generation in local grid. Using cloud computing, it is possible to

design and implement an optimized DSM program for utilities and consumers. In [92], authors

propose an efﬁcient energy management scheme for SG based on Cyber Physical System (CPS).

Smart devices are located at physical plane and controller on cyber plane. Nash equilibrium

approach is used to analyze the performance of proposed coalition. Proposed scheme provides

cost efﬁcient energy management solution during peak hours.

The cloud has huge computational resources, which are shared to facilitate the clients with

the provision of services. Due to these resources, the cloud provides IaaS, PaaS and SaaS instead

of client’s personal computing system. The maintenance of the private system is complex and

requires special skills to operate. This encourages clients from different domains to seek their

services from the cloud. The size and number of requests for the services on the cloud are huge,

which are managed with a variety of strategies. VMs on the cloud act as individual machines

with processors, memories and storage. Each VM executes tasks assigned to them. The authors

in [93] propose the breakdown of a framework into groups of tasks. Each group is called the

fragment of tasks, which is assigned to the VM for execution. The tasks are assigned to VMs

according to their capacity for optimized RT, PT and the cost. The simulations are performed

with three different conﬁgurations. The results validate the efﬁciency of RT, PT and the cost

as compared to baseline techniques. The speciﬁcation, size and number of VMs created on

physical unit affect the overall performance of the system. The challenge is to ﬁnd a mechanism

to create an appropriate number and sizes of VMs with the consideration of speciﬁcations of the

physical system and fragments.

2.2.3 The Cloud: Resource Allocation

There is a trade-off between the optimized scheduling of VMs for optimized resource utilization

and the cost. The prediction of future instances assists for optimized allocation of the tasks

on VMs. The authors in [94] use the Neural Network (NN) for prediction of instances and

validating them with the current instances. The predictive instances of tasks are efﬁciently

scheduled on VMs using the proposed modiﬁed PSO algorithm in [94]. Experimental results

show that the proposed algorithm outperforms the state-of-the-art optimization algorithms. The

authors also propose the heterogeneous infrastructure of cloud computing, which is used as

IaaS. The authors in [95], propose Parallel PSO (PPSO) algorithm for optimized selection of

VMs in the cloud data centers. The efﬁciency of the algorithm is analyzed by implementing it

on the predictive model for kidney disease. The proposed model outperforms the state-of-the-art

models considering the execution and response efﬁciency. Healthcare data is very sensitive and

the authors have not suggested the solution for the cure of error in the accuracy of prediction.

The author in [96] proposed a cloud load balancing algorithm for load balancing on cloud

and compare the results with previously used algorithms applied for the same scenario. The per-

formance of the proposed algorithm is better than the other algorithms. Therefore, load is bal-

anced when a multiple number of users logged in at the same time. The authors present a new

communication model using two models: the cloud based DR model and distributed DR model

for computing the communication efﬁciency with reference to optimal resource allocation [86].

This scheme procures the high cost by incorporating the high DR scenarios. The authors pre-

sented a cost oriented model for DSM by optimally allocating the cloud resources [87]. The

model has the ﬂexibility, which accommodates the resource allocation schemes to perform efﬁ-

ciently.

The authors proposed a new load balancing algorithm in [97]. For efﬁcient task allocation,

the load is balanced using a proposed algorithm. The mathematical model is used to allocate

low power tasks and others are allocated to human resources. However, the proposed algorithm

improves the throughput by reducing the tardiness. However, the authors in [98] compared mul-

tiple SBPs to estimate the PT. However, the round robin scheduling algorithm is used to compare

broker policies. Three SBPs are compared for RT and request PT. These policies include ser-

vice proximity, optimise RT and dynamically reconﬁgure with load SBPs. In [99], a Service

Oriented Middleware (SOM) model is proposed for the integration and utilization of fog and

cloud of things (CoTs). The SC ware summarize the components and services used for applica-

tions accessed through the service oriented model in the SC.

The author in [100] proposed a certiﬁcate-less provable data possession method. The method

is proposed for cloud based SG applications. The main the focus is to provide sufﬁcient storage,

processing capacity and security using data management system proposed to support SG ap-

plications. In addition, the authors in [101] used Wavelet Recurrent Neural Network (WRNN)

predictors. The solutions are given for a cloud distributed model and balance for energy manage-

ment is also achieved for available devices by means of prediction. The power is used according

to the requests coming from consumer side due to predictions made using WRNN predictors.

2.2.4 The Fog based Systems

Cloud and fog computing provide a virtual environment to provide efﬁcient resource sharing to

the connected consumers. These resources are allocated to increase the use of VMs instead of

using physical machines for energy consumption reduction. However, fog computing extends

the concept of cloud computing by reducing the load on the cloud. To maintain the load of

requests of connected consumers on cloud and fog, different load balancing algorithms are ap-

plied. These algorithms are used to schedule the requests and allocate the number of requests to

VMs. Mostly, both heuristic and static algorithms are used for load balancing among VMs as

in [102]-[105].

The fog is similar to the cloud; however, it has limited resources as compared to the cloud.

The fog is placed on edge of the network and closer to the end-devices. When a huge number of

requests of end-devices approach to the fog, the optimization for resource utilization becomes

a challenge. Various techniques have been proposed by the researchers for data security, real-

time response, efﬁcient processing and reduced cost. The SG also generates the huge number

of requests, which are delay sensitive and need to be entertained in real time. In this synopsis,

cloud-fog based system model for SG is proposed. In the scenario, the system model has six

regions. Each region has two clusters of residential buildings connected with two fogs. The goal

is to reduce RT, PT and the cost of VMs on the fogs. The load of requests is balanced on the

VMs to optimize PT, RT and the cost of VMs. The authors of [106] propose the algorithms for

a balanced load of requests allocation on VMs. The potential data center or the fog is selected

using ORT to route the requests on it. Routing the requests on the potential data center and

balancing the load of requests on VMs optimize the RT, PT and the cost of VMs. In this work,

an extension of [108] is proposed, in which hybridized SBP is introduced for potential data

center selection. The requests are routed on the potential data center. The proposed Modiﬁed

Honey Bee Colony (MHBC) algorithm allocates the requests on VMs in the data center or the

fog. The proposed SBP and load balancing algorithm optimize the RT, PT and VMs cost as

compared to the state-of-the-art techniques.

The cloud facilitates the consumers with: computing, storage and highly centralized con-

nectivity. However, it suffers from latency, security and downtime issues. For improving the

latency, ﬂexibility, resiliency and reliability of the cloud computing services, a new scheme is

proposed in [109]. The scheme is implemented using devices proﬁle for web services, which

presents energy management as a service using fog computing environment. Two energy man-

agement prototypes are developed in this methodology: the HEM prototype and MG energy

management prototype for cost minimization and latency reduction.

The energy management on the fog for SHs is a novel approach to optimize energy con-

sumption with direct or indirect autonomous control. In [110], energy management-as-a-service

over fog computing platform is proposed for SHs. The software and hardware for SHs’ energy

controlling architectures with efﬁcient utilization of fog computing resources are proposed.Fog

computing provides ﬂexibility, data privacy, interoperability and real time energy management.

Two prototypes are implemented to validate the reduction of implementation cost and time-to-

market.

The home appliances are categorized to form the subsystems by connecting with the con-

trollers. The controllers and the appliances are connected with the SM. The SM communicates

with the fog for controlling and scheduling the load of appliances. The authors of [111], pro-

pose the hardware and software architectures for fog computing. The fog provides services for

energy management for SHs. The fog based energy management service reduces the cost, RT

and PT as compared to the cloud. Similarly, in [112], cloud-based CPS for energy efﬁciency in

smartphones is proposed. Energy aware dynamic task scheduling algorithm is proposed to re-

duce the energy consumption in smart-phones. However, latency issue of the cloud compromise

the performance, which is challenging to resolve.

The fog and cloud can provide similar services; however, fog is newer research domain,

which makes its way to CPSs with IoT. Cloud has huge computational resources to process

extensively large data stored on remote and centralized data centers. However, data security

and remote access to the resources have reliability and latency issues [113]. The fog extends

the cloud services with reliability, data security and mobility of end-devices with local storage

and processing [113], [114]. Virtual resources of fog and cloud are efﬁciently allocated with

requests using load distribution algorithms. In [115], authors propose a heuristic algorithm

to allocate customers0requests on VMs of the cloud using the Coefﬁcient of Variance (CoV)

as the objective function. Simulation results verify the performance of proposed algorithm as

compared to ﬁve existing state-of-the-art algorithms. The authors consider the one-time frame

of the request; however, the behavior of the request changes in its processing lifetime.

The authors propose a Privacy Preserving Fog-enabled Aggregation (PPFA) architecture for

data security and efﬁcient energy consumption for data transmission in [116]. The fog node

collects data from SMs and estimates the cost of fog level aggregation; however, cloud or the

utility supplier calculates the total cost while aggregating all fogs. The transmission of data and

security techniques are the overhead on computing performance of fog and cloud. Performance

inefﬁciency compromises the cost, RT and PT which the authors of [116] have not considered.

The energy consumption of computing resources increases according to a load of requests to

be processed. The authors in [117], propose the joint optimization; for energy consumption

of resources and allocation of requests on VMs. Two heuristic algorithms are implemented

for scheduling workload on VMs for energy efﬁciency of resources. VMs generations and

degeneration take startup time and time for freeing the occupied resources. However, the energy

of resources have a direct relationship with the performance.

The trade-off between energy efﬁciency, system performance and the latency are the core

challenges of cloud based system. To overcome these challenges, the authors in [27] propose a

multi-layer fog architecture in context SC. The system is applicable for academics, businesses,

government organizations and different corporate companies in the smart city; however, leg-

islation is the challenge. The simulation results show that architecture is energy efﬁcient for

computing devices with high performance. Similarly, the authors in [118] address the chal-

lenges realted to the fog based system model in the context of the SC by proposing the Fog-

as-a-Service (FaaS) platform. The platform consists of two sub-system models based on the

information and the utility. The platform provides self-adaptation and composition of services

for end-users. The models are executable on end-devices (end-users0devices) and in the cloud.

The priority of information and context based ﬁltering by the FaaS proves effective. These are

capable to run on end-devices and in the cloud. The data from heterogeneous devices in the SC

has emerged the trade-off between the performance of processing and information accuracy.

2.2.5 The Fog: Resource Allocation

The authors in [119] propose energy-aware load balancing of equipments in smart factory using

fog based system. An improved PSO algorithm is proposed for efﬁcient energy consumption

model. The distributed scheduling of manufacturing cluster is resolved with proposed multi-

agent system. Similarly, to improve the performance of cloud and fog computing a variety of

heuristic and meta-heuristic techniques are implemented and have been proposed. In [120] and

[121], heuristic based load balancing schemes are proposed for fog computing resources. The

fogs are preferred over the clouds for (near) real-time applications and their efﬁcient perfor-

mances. Hence, optimized utilization of limited fog computing resources improves the perfor-

mance and minimize the computing cost.

Similarly, a Transmission Control Protocol/Internet Protocol (TCP/IP) based single hop net-

work is proposed in fog data centers with adoptive resource scheduler for energy efﬁciency

in [122]. The requests of trafﬁc arrive at fog data center where scheduler conﬁgures the VMs for

minimum energy dispatching. Moreover, requests are instantaneously processed and responded

to vehicular clients. In [123], a platform of fog-of-everything with Internet-of-everything is pro-

posed for the required satisfactory quality-of-service. The proposed algorithm in the platform

saves more energy as compared to existing state-of-the-art algorithms for the fog data centers.

Fog and Internet-of-everything are also integrated in [124] and design the fog-of-everything like

in [123]. However, the authors of [124] performed simulations to study the reduced energy and

enhanced the performance of fog computing for huge data.

2.2.6 The Cloud-Fog based Systems

A huge number of residential energy consumers are connected with the SG. The energy con-

sumers generate hundreds of thousands of requests to meet their demands with cost efﬁciency.

These requests need to be processed and respond back in real time for efﬁcient energy manage-

ment. Similarly, in various domains and scenarios, huge data is produced, which needs to be

processed and respond in real time. For instance, the authors in [114] propose cloud-edge based

system to compute extensively huge requests generating from the portable devices used for mi-

crobial studies. From the previous work done by the authors, it is suggested that edge computing

and end-devices should have a direct connection for efﬁcient processing and real time response.

The cloud to edge or fog computing system is effective for large scale communities and huge

data processing. However, the challenge is to choose a communication medium with appropri-

ate bandwidth for huge data transportation between cloud and fog computing nodes. So, energy

consumers also generate the huge requests and data every day to be efﬁciently processed and

responded.

The authors monitor the bulk power grids with grid cloud concept [125]. This concept is an

open source platform for real-time data estimation. In grid cloud, SG is supported on Amazon

web services and is similar to the cloud platform. The authors used cloud tools for cost re-

duction, software oriented redundancy to overcome the failures and different methods to secure

the sensitive data. The authors proposed a Privacy Preserving Fog-enabled Aggregation (PPFA)

architecture in [126]. The fog node collects data from SMs and estimates the cost on fog level

aggregation; however, cloud or the utility supplier calculates the total cost while aggregating all

fogs. In [127], authors conduct an analysis that shows, how the cloud based utilities improve the

SG ecosystem and how services on fog computing work for the same scenario. The algorithms

run on the fog while interplaying with the cloud and verify, if these are applicable for realistic

environment provided with delay free SG services.

The cloud computing has huge resources for centralized processing which is suitable for

processing of big data and delays oriented applications. So, an individual would prefer cloud

based services. However, for real-time applications and services, the cloud based system is un-

suitable. Hence, the authors of [128] propose the integration of cloud and fog based system

model for optimized RT and PT. The authors study the key challenges and propose their so-

lutions. The cloud-fog computation provides semi-centralized with near-real time computing

services. However, the key challenges are resource management, quality of service, features

of communication medium between fog and cloud as well as between fog and end-devices,

mobility, job scheduling, feature of IoT data, security and migration of cloud services on the

fog. In consideration of these challenging parameters, the authors propose a framework called

“iCloudFog”. It provides conﬁgurable integration between cloud and fog. The platform consid-

ers the type of communication medium between cloud and fog, features of IoT devices and data

security, etc.

With the development and invention of high bandwidth wired and wireless communication

medium, the end-computing-devices are becoming powerful too. The applications for the de-

vices need high processing with the storage. The end-users’ devices have limited power backup;

hence, the applications are processed on the high executing entity to save the power of end-

devices and meet the execution requirement of the applications [129]. For this purpose, tasks

from the devices are ofﬂoaded on the fog or the cloud using proposed intelligent middle-ware

service. The simulation results for various scenarios validate the efﬁciency of the proposed of-

ﬂoading service for computation, energy consumption and the cost. Similarly, a strategy for

energy and cost efﬁciency is proposed by [130] for mobile devices. Applications of mobile de-

vices are ofﬂoaded on to the fog for processing with reduced delays and payment cost. A three

queuing model is proposed with theoretical multi-objective optimization to reduce response and

processing delays with reduced billing cost. Extensive simulations show the effectiveness of

the proposed scheme by ofﬂoading such that execution and delays are minimized. Physical

equipment cost, installation and maintenance of a three layered system is expensive and costly.

In [131], comparative analysis of cloud and fog based system is proposed using mathematical

formulation considering the characteristics of fog and cloud in the context of IoT. The emission

of carbon-dioxide is proportional to energy consumption. It is concluded that the fog is suitable

for IoT based latency sensitive applications, the quality of service and eco-friendly as compared

to cloud computing platform.

The above discussion elaborates that the cloud computing system has virtually inﬁnite re-

sources to process huge or big data and provides services like infrastructure and platform on

pay-as-you-use bases. Virtual resources are created on physical resources for resource shar-

ing and efﬁcient service provision; however, virtual resources need efﬁcient management. The

authors of [93], [115], [94], [95] and [117] propose the variety of schemes and scenarios for

resource optimization according to the load of requests from

Table 1 Literature: Systems, Architectures and Frameworks

Technique Feature Tool Limitation

Proposed multi-tier

architecture [27]

Reduced energy

consumption of

whole platform,

subsumes of

devices communi-

cation, task routing

algorithms grantee

the platform

effectiveness

iFogSim Legislative issues and

challenges

Message oriented

middleware on

Instant message

protocol [90]

Shift cloud services

to the edge with se-

cured data

XML (extensible

Markup Language)

for web-services,

Java, Open source

Lightweight Di-

rectory Access

Protocol (OpenL-

DAP)

Middle-ware security

wall is necessary.

Challenge is to

overcome the time

complexity overhead.

Proposed scheduler

for VMs allocation

[93]

1.Improved PT; 2.

Improved RT; and

3. Reduced cost

Workﬂow schedul-

ing applying adapt-

able and dynamic

fragmentation

Hardware capacity

limitations

Prediction and

enhanced heuristic

technique [94]

Reduced cost,

Tasks prediction

for proposed algo-

rithm is efﬁcient

CloudAnalyst,

Python based

simulator

Speciﬁcations of phys-

ical and virtual re-

sources affect the per-

formance in real envi-

ronment.

Proposed PPSO,

NN prediction,

hybrid model [95]

VM optimization,

high prediction

accuracy of next

tasks, ﬂexible

proposed model

CloudSim, Win-

dows Azure

Flexible features, fre-

quent update the num-

ber of VMs which con-

sumes time for startup

and deallocation.

Architecture pro-

posed for cloud-

edge computation

[114]

1. Combining

cloud and edge;

2. Low power

processing for

portable devices; 3.

Large scale envi-

ronment and huge

data computation

MinION: genome

sequencing device.

Deepnano: data ﬁle

producer. Plato-

forms: XeonD

1540, Pentium

N3700, Pentium

J4205

Challenge of band-

width for communi-

cation medium, end

devices and edge

computing.

Requests distribu-

tion algorithm on

VMs [115]

Requests parti-

tioning, theoretical

evaluation for

hardness of the

partitioning

Simulator not spec-

iﬁed

Effects of resource

co-relation on perfor-

mance

PPFA, [116] Statistic aggrega-

tion on both; fog

and the cloud level

MATLAB R2016a Data security tech-

niques compromise

the performance and

system efﬁciency

Two heuristic

techniques for

deterministic and

random schedul-

ing, [117]

Resource optimiza-

tion, energy opti-

mization, job man-

agement for execu-

tion

Not speciﬁed Frequent resource and

job management com-

promise the system

time efﬁciency

Information and

utility based model

for FaaS, [118]

FaaS runs on

end-devices and

cloud, explored

trade-off between

computing speed

and accuracy

Distributed Node-

RED (D-NR),

OpenVolcano,

Linux Containers

(LXC), SaltStack

Trade-off between

processing perfor-

mance and informa-

tion accuracy

Proposed conﬁg-

urable framework

“iCloudFog” [128]

Agile integration

of cloud and fog

computing with

vast features

Not speciﬁed Dimensionality of net-

work and conﬁgura-

tion, quality of ser-

vices, resource man-

agement and localiza-

tion could not meet.

Middleware

technology for

ofﬂoading [129]

Off-loading on en-

ergy consumption,

number and sizes

of tasks, monitor-

ing services, fault

tolerance, integra-

tion and interoper-

ability

Wireless technolo-

gies, autonomous

applications,

visualization

techniques

Trade-off in number,

sizes of resources and

services, energy con-

sumption and band-

width

Proposed setup for

networking, energy

consumption, cost

and emission of

CO2, [130]

Reduced cost,

emission of CO2,

latency and im-

proved quality of

service

Not speciﬁed Bandwidth limitation

between end-devices

and the fog

Mathematical for-

mulation, [131]

Reduced energy

consumption, de-

lay and payment

cost

Not speciﬁed Limitations of the fog

resources, bandwidth

of wireless medium

end-users0devices. However, data security, energy consumption and latency are the chal-

lenges for cloud based systems.

The researchers encourage the cloud-to-fog based computing system for mobility, avail-

ability, latency sensitive applications and data security. However, the authors in [27] and [131]

propose fog based solution for latency and data sensitive applications. Moreover, the collocation

of end-devices with the fog optimize processing and minimize computing cost due to sharing

of tasks executions. However, resource limitation of fog cannot replace the cloud for huge data

from dispersed logical and geographical locations. In the view of the characteristics of cloud

and fog computing, a hybrid of both are proposed by researchers to fetch maximum beneﬁts.

The authors in [90] and [114], propose the connectivity mechanism of existing cloud with the

fog for latency sensitive applications. In [128], a cloud-fog based framework is proposed to

analyze the key challenges of integration of two computing environments. The authors of [116]

are implemented the privacy based aggregation of smart-meter data on the fog. The aggregation

of fog information and data on the cloud for secure data, reduced cost, energy consumption and

the cost are optimized. The literature discussed above is summarized in Table 1.

Table 2 Literature: Resource Allocation in Cloud and Fog

Technique Feature Limitation

Proposed load and pro-

cessing efﬁcient architec-

ture with two load balanc-

ing algorithms [96]

The load balancing ar-

chitecture validates perfor-

mance of virtual and physi-

cal resources of servers

The high number of con-

nections compromise the

performance

Proposed colorful ants

based load balancing

algorithm [97]

Dynamic load balancing,

HR ranking based tasks

queue

The time complexity of the

algorithm is high due to it-

erative nature and not ap-

propriate for huge requests

3 SBPs and 3 load balance

algorithms [98]

RT and PT using closest

data center policy is opti-

mized as compared to rest

Static requests data limit

the understanding of per-

formance with Closest

Data Center (CDC) policy

Proposed service oriented

architecture and design of

middleware for cloud and

fog computing in SC [99]

Mobility, heterogeneity,

real-time response and dta

security

Limitation of fog resource,

limit of number of devices

registration, Java has limi-

tation for implementation

Proposed management

architecture predictive sys-

tem for power generation

from integration system

and power consumption

using cloud distribution for

energy dispatch [100]

Real-time processing, pre-

diction of power generation

and load demand

Number of nodes between

power generation and

cloud node and between

cloud node to power

consumers cause latency

issues

Proposed hybrid of active

monitoring load balancing

and throttled [102]

Improved RT, PT, through-

put and reduced opera-

tional cost

Static data implementation

Proposed linear program-

ming based Dynamic

Weighted Live Migration

(DWLM) mechanism [103]

Runs information based

policy, improved migration

time, throughput and RT

Dynamic evaluation has

high time and space com-

plexity while LP waits un-

til dynamic migration eval-

uation is performed. Costly

and complex

Proposed fog computing as

energy management plat-

form [109]

Open source hardware and

software for customized

services, scalability, adapt-

ability, interoperability,

scalability and connectivity

of devices with fog, energy

management

System deployment of res-

idential sector is challeng-

ing due to low budget, op-

erational and maintenance

cost are additive cost and

important for residents.

Proposed an open source

GridCloud platform for

bulk interconnected power

grid [125]

Data security, reduced cost

and software based failure

overcome

Geographic diversity com-

promise performance of the

cloud, latency

Proposed cloud and fog

level aggregation for

SG [126]

Data security, robust, direct

link of smart meters with

fog node

Security key sharing for

huge number of users is

expensive, limitation of

downstream bandwidth

Proposed the fog based

model for SG [127]

Mix of fog and cloud com-

puting assures high com-

putation with security and

mobility

Redesign and development

of delay sensitive appli-

cations for fog node with

migration characteristics,

New SG service services

need to develop, security

challenges between cloud

and fog nodes

Proposed PSO based load

balancing algorithm [132]

Reduce task overhead and

enhance resource utiliza-

tion

Redundant and costly, eval-

uation of a VM has to wait

until ﬁrst completes

Proposed autonomous

agent based load balancing

algorithm [133]

Increase throughput, re-

source utilization, reliabil-

ity and scalable

Dynamic iterative tech-

nique has high time and

space complexity

The literature discussed above is summarized in Table 2. From the literature, it is analyzed

that fog computing helps to maintain and reduce the load of cloud efﬁciently. Multiple algo-

rithms are introduced for load balancing and new SBPs are proposed in research articles. How-

ever, the results are improved and cost is reduced with efﬁcient utilization of VMs as compared

to using physical machines.

2.2.7 Cloud and Fog based Systems with Energy Sharing Services

Energy production considering the environmental pollution, green-house effect and mainte-

nance of grid equipments propose the efﬁcient energy management on demand side. In the

last decades, various intelligent solutions of energy management for the residential sector have

been proposed. In [134], the authors propose the priority based model for demand side to shave

the peak-loads from on on-peak hours to off-peak hours. The proposed solution saves the energy

consumption cost. However, only three appliances are scheduled for a SH. Similarly, authors in

[135] implement the CSA and strawberry algorithms for scheduling of SH appliances.

In the aforementioned researches, the researchers propose solutions for cost efﬁciency of in-

dividual SHs; however, in communal scenarios, the parameters related to mutual interaction for

multiple SHs energy optimization are considered. In a community, SHs have RES, power stor-

age systems and the combination of different power systems. Hence, studies are made for cost

efﬁcient energy generation, consumption and sharing in the community. For multiple homes,

authors in [136] propose a transactive energy framework for sharing private MGs of multiple

SHs for power consumption rather than trading with the utility. The prosumers are encouraged

to participate in the proposed framework. However, the authors do not suggest how many MGs

are feasible for the framework. Authors in [137] propose a power economy sharing model using

BESS. The proposed model is feasible only for a small community; however, authors do not

explain how big community or communities can beneﬁt from the system.

Authors in [138] propose an optimal planning for connectivity of communal MGs for eco-

nomics, reliability, variability, adaptability and uncertainties of operations performed. The in-

terconnection planning is applied to only six MGs in a cluster form. The proposed plan is

feasible for practical MG applications. While studying the economic aspect, they discuss the

only capital cost to install and connect MGs. However, consumer beneﬁts are important. SHs in

communities are equipped with RES, storage systems and are also connected with utility for an

emergency. The SHs without power systems have to use utility power only. However, SHs with

personal power backup systems can share with other SHs in a community. The authors in [139]

have proposed similar sharing approach using game based coalition in the community to reduce

electricity consumption cost. Communal participants reduce the cost, however, a computational

module for pricing is not explained. Moreover, feasibility of energy optimization according to

the size of the community is important to consider.

Sharing of communal energy resources are important to reduce cost instead of increasing

load on utility. Sharing of MGs with RESs are risky due to their intermittent nature [137]. MGs

with RES are expensive and difﬁcult to maintain. ESS along with RES makes overall expensive,

complex and intermittent system which reduces the lifespan of the BESS [139]. Moreover, more

the participants in communal power systems generate a number of requests to get processed

and entertained. The delaying and expensive computational environments are unﬁt to deploy.

Authors in [140] propose the cloud based plug-in electric vehicle charging and discharging. A

huge number of requests from plug-in stations, electrical vehicles, RES and HEMS are send to

the cloud for computation. Authors in [141] propose a cloud based future generation power grid

applications considering data, computation and proposed framework of direct load control for

power grid application. The cloud computing is feasible for computation of huge data; however,

it suffers from delay, data security and deployment issues [142].

Authors in [106] propose a cloud-fog based system model for the SG. A number of build-

ings with RESs are connected with the fog layer. Each building consists of a number of SHs

which generates requests for power. The requests are processed on the fog. Two scenarios are

simulated using four resource sharing algorithms to enhance RT, PT and reduce the cost of re-

source utilization. The simulations show the efﬁciency of fog with 5 VMs as compared to the

fog with 2 VMs. RT, PT and the cost are optimized. Moreover, proposed hybrid algorithm for

Table 3 Literature: SH and Community based Systems with RESs, ESSs and Utility

Technique Objective Limitation

Two tier cloud based demand

side management [14]

Reduce cost, improve power

grid performance

There is always computa-

tional cost which is important

to calculate for the system.

VM allocation algorithms on

cloud-fog based system [106]

Enhance computational per-

formance and reduce the

computing cost

Beneﬁts to consumers for

computing environments is

important.

GA, EDE, BPSO and OSR

heuristic algorithms [134]

Reduce power consumption

cost for a SH

Limited scope: Small infras-

tructure of single SH with

only three appliances

Cuckoo Search and Straw-

berry heuristic algorithms

[135]

Reduce power consumption

cost for SHs using miroc grid

and ESS

Compromised user satisfac-

tion and utilization of extra

storage are not discussed

Coalition of MGs [136] Gaining competitiveness in

power market

Effects of intermittent nature

of RESs based MG on coali-

tion, Physical constraints of

network

Multi-agent based Uniﬁed

Energy Storage System

(UESS) of communal SHs

[137]

Reduction of communal cost

for power consumption

Limited size community

Interconnection plan of MGs

cluster [138]

Reduce cost and power on

demand side and applicabil-

ity of the plan in practical ap-

plications

Only installation cost, rem-

edy for intermittent nature of

MG not proposed

Two game theory based com-

munal power sharing of RES

and ESS [139]

Reduce communal cost and

trading without ESS SHs

within community

Limitation of proposed sys-

tem with respect to size of

cooperating RESS and ESS

owners

Electrical vehicle based

energy storage using GA and

multi-constrained integer

programming [140]

Reduce energy purchasing by

maximum use of ESS for cost

efﬁciency

Surplus storage is kept rather

utilization

Discussion over the cloud

based future generation

SGs[141]

Compute and data intensive

applications

Cybersecurity, practical im-

plementation and complex-

ity of infrastructure are chal-

lenging

Survey: fog/edge computing

covers limitations of cloud

computing [142]

Near real time response, se-

curity and simplicity

Resources and infrastructural

limitations

Communal battery storage

system with household bat-

teries and PV [143]

Self-sufﬁcient power for

community

Communal battery system

suffers more when PV pan-

els fall short than individual

household.

Column and constraints al-

gorithms along with experi-

ments [144]

Self power generations for

trade and self consumption

optimization

Storage system and their ef-

fects on renewable energy

system need to study

VMs allocation is efﬁcient as compared to state-of-the-art algorithms.

The integration of PV power generation and battery storage system for SHs in the commu-

nity are proposed in [143]. The batteries are explained considering their types and capacities.

Authors concluded that BESS with PV develops a self-sufﬁcient energy community. Moreover,

the system is more economical than an individual household system. The intermittent nature

of PV sources and weak power generation affect the efﬁciency of batteries storing. However,

for individual SH PV power generating system is effective. In [14], two tier of a cloud based

system model for the SG is proposed. Regional or edge cloud facilitates the SHs which have

MGs and energy storage systems. The cloud on the second tier covers the multiple regions by

connecting with the regional clouds. The proposed system reduces the cost for consumers and

improves power grid performance.

The RESs are integrated with the main grid for trading in [144]. The intermittency of RESs

is tackled with proposed probable identiﬁcation solution. The solar power generators, power

storage system and power exchange with the main grid for cost optimization are implemented

with the proposed algorithm. Moreover, charging and discharging, islanding operation, line

switching and power trades with robust optimization formulation is proposed. The proposed

system is validated by implementing different test cases. However, renewable PV power gener-

ation required complex and expensive deployment to trade with the main grid.

Authors in [134] and [135] proposed load optimization techniques on SH appliances for cost

optimization. Authors in [136], [137] and [144] propose the joining of micro power production

units and storage systems for cost reduction at community level. However, the big sized com-

munity, as well as dispersed SHs can be combined using the cloud infrastructures [141], [106],

[143]. Delay and data security are core issues of the cloud [142]; however, two tier cloud for

SG tackles delay and data security issues [14]. ESSs are cheaper than the RES based MGs as

well as most of SHs in communities, which already possess storage systems so, uniﬁcation of

these systems has the potential to make a small community self-sufﬁcient [137]. In this synop-

sis, BESSs of SHs in a community are integrated using the fog to form UESS. There are twelve

communities each has a fog for Service-as-a-Power-Economy-Sharing by designing the UESS.

These fogs are connected with a cloud for sharing utility pricing and permanent data storage of

fogs. The literature discussed is summarized in Table 3.

3 Problem statement

DSM’s strategy of load shifting is generally adopted to minimize the cost of power consumption

by shifting the load from on-peak to off-peak time [145]. In the literature, variety of heuristic

techniques are proposed to schedule the electric appliances in order to reduce the cost of power

consumption. Moreover, different hybridization schemes are available in the literature to ex-

plore and exploit the search space to achieve better optimality. However, complexities of these

heuristic algorithms have not been considered to analyze the performance efﬁciency. The algo-

rithms with high complexity may provide efﬁcient scheduling of appliances; however, execution

and time complexities are challenging to tackle [146]. These algorithms are loaded in energy

management controller which is installed in a home or building to manage, schedule and control

the electric appliances. Moreover, the integration of RESs is being widely promoted across the

globe [147]. The surplus energy generated by RESs is being shared among the energy deﬁcients

through peer-to-peer energy sharing. In peer-to-peer energy sharing, a local market platform is

provided where all the prosumers share power without the involvement of a third party [147].

The peer-to-peer sharing requires an efﬁcient system with minimum delay and elimination of

single point failure [148]. Cloud computing provides efﬁcient energy management platform

for centralized and distributed environments with the cure of single point failure [149], [150].

This motivates the companies to use the resources: hardware, storage, applications, etc. without

huge investment to purchase, install and maintain system [151]. The cloud has virtually inﬁ-

nite resources and provides efﬁcient energy management in SG [152]; however, the cloud based

infrastructure has latency issues [153]. The fog provides services on the edge of the network

with direct connectivity of end-users. The fog based system reduced the latency. A huge load

of requests or tasks creates the performance bottleneck for fog based system because of limited

resources [154]. An efﬁcient resource utilization is required to minimize computing cost, RT,

PT and to provide optimal energy management services for communities. In real world, renew-

able energy is integrated under the signed contract between power utility and prosumers [155];

however, PT increases with the expansion of community size [156]. From the end-user’s aspect,

longer latency (sum of PT and RT) can degrade the energy management service.

4 Proposed System Models

Two types of system models are proposed considering islanded SH in a region and the commu-

nities in a city (rigid and ﬂexible). In Section 4.1, appliance scheduling techniques are proposed

for islanded SH. However, in sections 4.2, 4.3, 4.4, 4.5 and 4.6 cloud-fog based system models

are proposed to provide energy management services to the communities. Various scenarios for

efﬁcient computing resource utilization to provide efﬁcient services have been proposed in the

manuscript. The cloud has huge computing resources to provide efﬁcient computing services;

however, increasing services with huge computation, communication and performing intelligent

tasks are challenging for the performance of cloud based systems. To overcome the challenges,

processing, storage and network facilities are ofﬂoaded in fog, which act as intermediate com-

puting layer between cloud and end-users or end-devices [157]. The direct connectivity of

end-devices with computing node (fog) through high bandwidth wired and wireless medium

provides near real time services [158]. In SG, real time services are indispensable [45], [159]-

[161]. The intelligent energy management services in real time, especially for communities, the

fog based system is appropriate and suitable for SG scenarios [109], [162]-[163].

4.1 1st Proposed System Model: Islanded SH

The EMS consists of SSM and DSM. The supply side generates, transmits, distributes, main-

tains and controls the electricity. The prices of power consumption are also deﬁned at supply

side, which depend upon generation, supply, control, maintenance and demand. The price rises

with the increase in demand because of high power generation and high cost is required for

the maintenance. The DSM provides a variety of scenario based cost efﬁcient solutions. The

popular strategy to reduce maximum cost is shifting the appliances load from on-peak to off-

peak time. DSM controls the operations of appliances to reduce the bills for the consumer. The

appliances are divided into classes according to their nature of operation and consumer’s prior-

ity request for load. In proposed system model, a SH with eight appliances is connected with

the utility. The appliances are categorized into “A” and “B” classes. The class “A” consists of

shiftable appliances and class “B” consists of non-shiftable appliances. The class “A” has an

electrical geyser, an air conditioner, a washing machine, a dishwasher and a cloth dryer. The

class “B” has a portable heater, a microwave and a fridge. The class “A” is scheduled to opti-

mize energy consumption while class “B” is unscheduled. Shiftable appliances are scheduled

to meet the speciﬁc goal(s); for example, cost reduction, minimize waiting time and maximize

user satisfaction. In this work, the main focus is to reduce cost with proposed schemes for a SH.

In the proposed system model; Figure 1, an EMC is a hardware device, which is placed in a SH

which provides an interface to the user. In the real world, the proposed scheduling algorithms

are installed in EMC and the user can choose any scheduler to optimize SH appliances. The

smart meter is connected with EMC and the utility to measure the energy consumption of SH

and request for power load from the utility.

Energy

Management

Controller

Smart

Meter

User Interface

Class A

Class B

Class A:

Shiftable

Appliances

Class B:

Non-shiftable

Appliances

Washing Machine

Dish Washe r

Cloth Dryer

Electrical G eyser

Air Conditione r

Portable He at er

Microwav e

Fridge

Utility

Two-way

Communication

Smart Home

User

Power supply

Figure 1. 1st Proposed System Model: Islanded SH

4.1.1 Classes of Appliances

Operating behavior of appliances in time space depends on the consumer’s requirement. If

an appliance is shifted in low pricing time it reduces the power consumption cost; however,

user comfort is compromised. Hence, there is a trade-off between the cost and user comfort.

Every appliance in the home cannot be shifted for example, fridge remains switched-on for a

whole day because food preservation is very important. The use of a washing machine can be

shifted from high-pricing to low-pricing hour; however, it compromises the user comfort. The

shifting of appliances from on-peak to off-peak hours reduces the power consumption cost by

compromising the consumer’s satisfaction. In the proposed scenario, domestic appliances are

classiﬁed into these two classes that are described in Table 4.

Table 4 Classes for home appliances

Shiftable Loads Non-Shiftable Loads

Electrical Geyser Portable Heater

Air Conditioner Microwave

Washing Machine Fridge

Dish Washer

Cloth Dryer

Shiftable Appliances

The appliances that are schedulable from consumer’s required time to any time-slot of a day

are called shiftable appliance. The load of an appliance in particular time slot shows the status

of that appliance and the power rating is shown by Pap

r. The status is “1”, if the appliance is ON

and the status is “0” if the appliance is OFF. The load of the shiftable appliance is calculated as

Eq. 1.

Lap

t=Pap

r×ωap

t,(1)

where Lap

tis the load of a shiftable appliance for speciﬁc time-slot t.ωap

tis the status of an

appliance in speciﬁc time-slot t. Suppose, Asis the set of all shiftable appliances and λAsis

power rating of given shiftable appliances, the total load in a day is the sum of all power rating

of shiftable appliances times statuses for all time-slots of a day. The Eq. 2 calculates the total

load of all shiftable appliances for the time-slots in a day.

LAs

∑

t=1

∑

ap=1

Pap

r×ωap

t,(2)

where, LAs

tis the total load of all shiftable appliances and ωtis the statuses of appliance in

a day. Total number of time-slots in a day is Tand Asis total number of shiftable appliances.

Cost of an appliance for a given time-slot is the product of load and unit price for that time-slot,

as given in Eq. 3,

Cap

t=Pap

r×γap

t×ωap

t,(3)

where, Cap

tis cost of shiftable appliance for speciﬁc time-slot and γap

tis the tariff rate in given

time-slot. The sum of all costs for all time-slots is the total cost for a day as computed in Eq. 4.

CAs

∑

t=1

∑

ap=1

Pap

r×γt×ωt,(4)

Where, γtis the price in given time-slot.

Shiftable appliances can be shifted to other time-slots in a speciﬁc day. For example, wash-

ing machine, dish washer. The load and cost of all appliances are calculated accordingly. In

proposed scenario, electrical geyser, air conditioner, washing machine, dishwasher, cloth dryer

are shiftable appliances for the home.

Non-shiftable Appliances

The appliances which have to operate for ﬁxed time-slot and cannot shift to other time-slot

are called non-shiftable appliances. In the proposed scenario, portable heater, microwave and

fridge are non-shiftable. It is assumed that the operation time of these appliances is known in

advance, as given in [1]. Let, ANsis the set of non-shiftable appliances and φ∈ANs then the

load for a non-shiftable appliance Lφ

tfor a speciﬁc time-slot is given in Eq. 5,

Lφ

t=Pφ

r×ωφ

t,(5)

where, Lφ

tis the power rating of a non-shiftable appliance and ωφ

tis the status (ON/OFF) of it.

The total load of all non-shiftable appliances is calculated as in Eq. 6

LANs

∑

t=1

ANs

∑

ap=1

λANs ×ωt,(6)

λANsis the power rating for all non-shiftable appliances and omegatis the status (ON/OFF) of

appliances. The total cost of all non-shiftable appliances is the product of load and tariff rate.

Hence, the total cost of non-shiftable appliance is calculated as in Eq. 7.

CANs

∑

t=1

ANs

∑

φ=1

Pφ

r×γφ

t×ωφ

t,(7)

Where, CANs

tis the total cost and γtis the tariff rates.

4.1.2 Proposed Techniques

In this research work, four hybrid schemes are proposed. The two parts of Elephant Herding Op-

timization (EHO) are hybridized with the state-of-the-art GA, Fireﬂy Algorithm (FA), Bacteria

Foraging Algorithm (BFA) and BPSO. The simulations are performed to schedule the SH appli-

ances to minimize the cost with optimized load using DA-RTP, CPP and IBR pricing schemes.

It is assumed that the intervals of operation for each appliance are equal. The implementation

is performed with OTIs of 60, 30 and 10 minutes. With OTI of 60 minutes, the appliances start

operation at the beginning of an hour and complete at the end of the hour (completion of 60th

minute). Similarly, these assumptions are made for OTIs of 30 and 10 minutes. If the appli-

ance is turned ON, it must take complete OTI (maybe 10, 30 or 60 minutes). However, in 60

minutes OTI case, if the operation of an appliance is completed in 20 minutes in the real world,

the remaining time would be wasted. The data for appliances: their operation time and power

ratings are taken from [66]. The pricing signals like IBR, CPP and DA-RTP are taken from [1]

and [134]. The simulation results of each proposed schemes are analyzed and discussed in the

following sub-sections.

EHO Algorithm

Elephants are social animals and they live in the form of herd. They maintain herding rules.

A herd has more than one clans and each clan lives under the leadership of a female called,

matriarch. The matriarch is responsible to keep the clan under herding rules. The females and

calves live in the main family group; however, males live separated and maintain the bond with

their main family group using low frequency vibrations. The vibrations are produced by tapping

the ground with their feet. The male calves at their adulthood are separated from the main family

group and are forced to join other males. [164].

The two characteristics of elephant herding rules are very important: making the clans and

separating the males from the main family group. These two steps of elephant herding rules

are incorporated with GA, FA, BFA and BPSO. The ﬁrst step is used to divide the population

of a problem into sub-populations (clans) and in the second step, worst elements are discarded

from the clans or best elements are selected. The elements (position of an elephant) of a clan

are updated under the inﬂuence of the best element (matriarch) in the clan. The elephant Efrom

a clan updates its position using the Eq. 8.

new,Clani,E=P

Clani,E+β×(P

Clanbest ,E−P

Clani,E)×w,(8)

where, P

new,Clani,Eis the updated position of the elephant from Clani.P

Clani,Eis the old position

of the elephant E. The P

Clanbest ,Eis the current position of matriarch, which is the ﬁttest in clan.

βis the inﬂuence of matriarch with scale factor [0,1].wis used for uniform distribution having

range of [0,1]. In simulations, the random function is used to generate βand w.

Algorithm 1 EHO-Clans Population Update

1: for Clani=1 : nClans (for all clans) do

2: for E=1 : Cpop (All elephants in the clan) do

3: Update Clani,Eand generate P

new,Clani,Eusing Eq. 8

4: if Clani,E>Clanbest,Ethen

5: Update Clani,Eand generate P

new,Clani,Eusing Eq. 9

6: end if

7: end for

8: end for

If elephant Efrom the clan is best, then matriarch is updated using Eq. 9,

new,Clani,E=γ×P

Clancent er,E,(9)

Where γis the factor of [0,1]to determine the inﬂuence of the central elephant of the clan

Clancent er,Eto update the position of best elephant; P

new,Clani,E.

Algorithm 2 EHO-Selecting Best

1: for Clani=1 : nClans (for all clans) do

2: Find the best elephant using Eq. 9

3: end for

The two major steps of EHO; Algorithm 1 and Algorithm 2 are incorporated with GA, FA,

BFA and BPSO for efﬁcient scheduling the SH appliances for cost reduction with optimized

load consumption. The proposed hybrid algorithms are explained in the following subsections.

EHO-GA

GA is a bio-inspired evolutionary natural selection algorithm which is proposed by John

Holland in 1960 [165]. The evolutionary steps comprise of crossover and mutation, which trans-

form the genetic characteristics of a gene. Evolutionary steps are initiated with the population

generation and selection criteria. The initial population is generated randomly according to the

problem and selection of elements is performed by the ﬁtness process. The population is evalu-

ated with the ﬁtness function. The elements are selected on which genetic operators; crossover

and mutation are performed, which generate next generation. In GA, if selected candidates have

similar value or rank, then the crossover process leads to a less optimal solution. The scalabil-

ity of GA is compromised for complex problems. The mutation process is affected by a large

number of elements which increases the search space exponentially. For many problems, the

stopping criteria is not clear. Moreover, it has a tendency towards converge local optima.

In proposed hybrid EHO-GA, the population is initialized and evaluated for positions of el-

ements using the earliest steps of GA. The evaluated population is fed to the ﬁrst step of EHO.

The wnumber of clans are created of npopulation. The population of each clan is updated and

are converged. The elements of each clan are updated under the inﬂuence of matriarch (best

ﬁtted element in the clan). If an element is better than the matriarch, then it takes the position of

the matriarch. In the second step, worst (unﬁt) elements are discarded and remaining elements

are the best (most ﬁtted) elements. These updated clans are converged and form an updated

population. The crossover and mutation operations of GA are implemented on updated popula-

tion. The stopping condition is applied until the selection of the appropriate (best) solution. The

efﬁciency of optimization and time complexity is enhanced with convergence of GA and EHO

steps. The probability of crossover is kept 0.9 while for mutation it is 0.1. The steps of EHO-GA

are elaborated in Algorithm 3. The time complexity of the proposed (EHO-GA) algorithm de-

pends on the total population nand the number of clans w(subpopulations). In proposed system

Algorithm 3 EHO-GA

1: Initialize the population n

2: for i=1 : ndo

3: Evaluate the population

4: end for

5: wclans (subpopulations) of dsize=n/w

6: while MaxGeneration do

7: Generate new population using algorithm 1

8: for i=1 : w; For Number of Clans do

9: for j=1 : d; For all elephants in Clan ido

10: Evaluate jth elephant in Clani

11: end for

12: end for

13: Cln=Converge evaluated Clans

14: Evaluate Fitness of population (Cln)

15: end while

16: Lbp=Produce list of Best positions

17: CLbp=Calculate costs for list of Best positions (scheduled positions)

18: Update Best positions using Algorithm 2 from (Cln)

19: Conv=Converge (Cln) and (Lb p)

20: for k=1 : IntrpApp; For all interruptible appliances do

21: for m=1 : Positions; For all positions for a day for kt h Intr pApp do

22: CrossOver(Conv[m], rand(CLbp))

23: end for

24: end for

model the complexity of EHO-GA is O(n×w), where n6=wand size of population n> size of

a clan d. The time complexity of EHO-GA is smaller than GA, which is O(g×n×m). Where,

“g” is the number of generations and “m” is the size of individual.

EHO-FA

FA is inspired by the ﬂashing pattern of ﬁreﬂies and proposed by Xin-She Yang et al. [166].

FA is inspired by the ﬂashing pattern of ﬁreﬂies. The ﬂashes are used to attract the potential

mating partner and for protection. The intensity of luminosity is variable from the distance,

which is inversely proportional to the distance. The attraction of potential partner makes a

social interaction by solving a swarm problem with optimization. Solving the attractiveness of

a ﬁreﬂy to intensive ﬂashing one formulates objective function or the artiﬁcial ﬁreﬂy solution.

FA is ﬂexible and has the tendency to integrate with other optimization techniques to develop

hybrid schemes. In the proposed hybrid algorithm, the population is updated using EHO sub-

steps; Algorithms 1 and 2. The population is inputted to FA. The steps of this hybrid algorithm;

EHO-FA, are explained in Algorithm 4.

In proposed hybrid technique, initial population generated by FA is updated using two steps

of EHO. First step has two sub-steps which are selected conditionally. In ﬁrst step, population

is divided into two subpopulations; each is termed as clan. Each clan has best ﬁtted element and

named as ‘matriarch’. Elements of clans are updated according to the position of the matriarch.

The second level is selected when clan has better elements than matriarch. The matriarch is

updated with best ﬁtted element from the clan. Elements of wclans are fed to the main execution

of regular FA. In second part of EHO algorithm, worst elements are separated leaving behind

best elements in each clan. The best positions are then converged. This hybrid algorithm is

Algorithm 4 EHO-FA

1: Calculating Objective function f(X),X= (x1,x2, ....xT

2: Generate initial population of ﬁreﬂies Xi(i=1,2,....n)

3: Light intensity Iifor Xiis determined by f(xi)

4: coeﬁcient of light obsorption is deﬁned (β)

5: Divide population into wequal subpopulations (wclans)

6: UCc=Update clans using Algorithm 1 and converge the updated clans

7: Bps=Find best positions using Algorithm 2

8: while l<=MaxGeneration do

9: for i=1 : n, all n ﬁreﬂies (UCc)do

10: for j=1 : iall n ﬁreﬂies do

11: if (Ij>Ii),move i toward j then

12: Distance rvaries the attractiveness

13: Find new solutions and update light intensity f(xi)

14: end if

15: end for

16: end for

17: Idx=Find current best and store in an index

18: CIdx=Calculate the costs for (Idx)

19: Mb p=Merge best positions (Bps) and (Idx)

20: for k=1 : length(NApp >AvgPr )do

21: For NApp appliances greater than average power rating AvgPr

22: for m=1 : BT ; for length of Burst Time (BT )for kthshiftable appliance in a day

23: Select locations from (Mb p) (priority) minimum cost (CId x)

24: end for

25: end for

26: end while

described in Algorithm 4.

In the steps of Algorithm 4, the initial population is divided into wsub-populations and

update using Algorithm 1. The best elements of the sub-populations or each clan are selected

using Algorithm 2. The best elements are kept and unﬁtted are discarded. Each sub-population is

inputted to FA, which generates the best elements. These best elements from all clans are stored

in a list. The cost of each element is also maintained. The list is merged with best elements

generated from Algorithm 2 and form new updated population. The appliances which have

higher power rating than the average of all shiftable appliances are rescheduled in last phase

of proposed EHO-FA. These appliances are repositioned at minimum cost locations (already

determined). The time complexity of proposed EHO-FA depends on BP, which is determined

using Algorithm 2 and total updated population n. However, BP 6=nand the time complexity of

proposed EHO-FA is O(BP ×n).

EHO-BPSO

BPSO is used for binary discrete search space and is proposed by [75]. BPSO is used for

binary discrete search space. However, PSO is used for the continuous problem. The logical

characteristics of PSO and BPSO remain intact by forcing the velocity either 0 or 1 (in BPSO)

instead of between 0 and 1 (in PSO). In the proposed EHO-BPSO, the population is initialized.

The ﬁtness of each particle is evaluated. The population is updated by evaluating every particle

with currently ﬁtted particle. The velocity and position of each particle is updated. This returns

the list of updated (best) particle using BPSO. The cost of each particle is calculated in the

list. The list is divided into wsub-populations or clans and is updated using Algorithm 1. The

update is performed under the inﬂuence of ‘matriarch’ (best in the clan). If the particle has

better value as compared to the matriarch, then particle becomes a matriarch. The best elements

are selected from each clan using Algorithm 2. The resultant of all clans are converged. The

cost of each element is calculated and the appliances with high power rating than the average of

all appliances are scheduled into the lesser cost time-slots. This hybridized result provides cost

optimized solution.

Algorithm 5 EHO-BPSO

1: Initialize all Tparticles with (n) dimensions

2: while MaxGeneration do

3: for i=1 : Tdo

4: Evaluate ﬁtness values for each particle Ti

5: if Tipart ical is bett er than f itness value then

6: Set current particle as BEST

7: end if

8: end for

9: for i=1 : m Select global BE ST f rom all particals do

10: Update velocity of particle (between 0−1)

11: Update Position of particle

12: end for

13: end while

14: Lup= Return list of updated positions of particles

15: CLup=Calculate cost (Lup)

16: Divide list (Lup) into wequal subpopulations (Clans)

17: Update each Clan using algorithm (1)

18: Find best positions using algorithm (2)

19: Bp=Find best positions using algorithm (2)

20: Cp=Converge list (Lup) and best positions (Bp)

21: for k=1 : length(NApp >AvgPr ); Appliances NApp, with greater than average power rating

AvgPr do

22: for m=1 : BT ; for length of Burst Time (BT)for kt h for a day do

23: Select positions from (Cp) prefer minimum scheduled cost (CLup)

24: end for

25: end for

The evaluation of population and the update are performed separately, which do not effect

the running time. However, time complexity of proposed algorithm depends on the number of

appliances, which has higher power rating as compared to average power rating of all shiftable

appliances length(NApp >AvgPr)and their burst time BT . The time complexity of proposed

EHO-BPSO is O(length(NApp >AvgPr)×BT ).

EHO-BFA

BFA is inspired by the food searching behavior of bacteria species ‘E.coli’. In the process of

foraging, the bacterium searches the food in its environment and communicates with the neigh-

boring bacteria to announce the availability of food, which forms the swarm characteristics.

Following swarm behaviors are observed;

• Bacteria are dispersed around the food.

Algorithm 6 EHO-BFA

1: Initialize all mpopulation

2: for i=1 : mdo

3: Fitness evaluation of each Bacterium mi

4: end for

5: while MaxGeneration do

6: for j=1 : mdo

7: Discard and introduce new for unﬁt positions

8: end for

9: for k=1 : mdo

10: Calculate Fitness and Tumble in radom direction [-1, 1]

11: Calculating new direction (position)

12: Evaluate the Fitness for new position

13: if LastFitness <Cureent then

14: Replace LastFitness with Cureent position

15: Else

16: Assign random direction (new position)

17: end if

18: Evaluate Fitness for new position-select Best position

19: end for

20: Lbp=Return list of Best positions

21: Cbp=Calculate cost of list Best positions

22: Divide Best (Lbp) into wsub-populations (Clans)

23: Update each Clan using Algorithm (1)

24: Fbp=Find best positions using Algorithm (2)

25: LFbp=Converge (Lbp) and (F b p)

26: for k=1 : length(NApp >AvgPr )do

27: For NApp appliances greater than average power rating AvgPr

28: for m=1 : BT ; for length of Burst Time (BT) for kt h for a day do

29: Select positions from (LFbp) prefer (priority) minimum cost (Cbp)

30: end for

31: end for

32: end while

• A bacterium locates the food in its surroundings by chemotaxis motion (tumble and swim-

ming).

• The bacterium with scarce food dies and disappears.

• The bacterium which successfully accesses the food survives and reproduces. It also

invites other by producing chemicals.

• The bacteria disperse again for new food resources.

In proposed EHO-BFA, the population is initialized then ﬁtness of randomly selected bac-

terium is evaluated. Then steps of BFA are applied; discard unﬁt bacteria, perform tumble and

swim. On every step of tumble or swimming, either the bacterium is closer to food or away

from it. The population is updated on each tumble or swimming step. The steps are performed

for the whole population and it returns the list of best bacteria. The list is new population which

is divided into wsub-population or clans. The subpopulation is updated using Algorithm 1. The

update is performed under the inﬂuence of ‘matriarch’ (local best). If any other element is best

ﬁtted than matriarch then matriarch is updated. Each clan is updated and Algorithm 2 ﬁnds

only best and discard remaining unwanted elements from the population. The resultant of each

clan are converged to form new list and cost of listed elements is calculated. The appliances,

which have power rating more than the average of all appliances are scheduled into less cost

time-slots. Hence, appliances are scheduled with the best positions. The steps of EHO-BFA

are given in Algorithm 6. The time complexity of proposed EHO-BFA depends on number of

appliances, which have power rating greater than the average power rating of all shiftable appli-

ances length(NApp >AvgPr)and burst time BT of those appliances. Hence, the complexity of

EHO-BFA is O(length(NApp >AvgPr )×BP).

The scheduling behaviors of BFA and FA are almost similar due to conceptual similarity. In

BFA, attractiveness is due to chemical gradient in environment and in FA attractiveness is due

to luminosity of ﬂashes. In proposed EHO-BFA and EHO-FA, this behavior remains dominant.

4.2 2nd Proposed System Model: Rigid Community (Single Building-Single Fog)

In this research work, a three-layer system model is proposed for rigid community connected

with single fog for energy management, as shown in Figure 2. The clusters of residential build-

ings exist at the end-user layer. The cluster generates a number of requests for their energy

consumption. Each cluster is attached with a fog in the middle-ware layer. The fog receives

requests from the cluster for processing. MG with RESs and storage system is placed near each

fog. The SBP routes the trafﬁc of requests in potential fog server or data center. The load bal-

ancing algorithm efﬁciently allocates the requests on VMs in the data center. The recurring cost

of MGs and computational cost of the fogs form the system cost and added in consumers energy

consumption bills. The fogs are connected with core or cloud layer where necessary data from

the fogs are sent for permanent storage. The cloud also broadcasts the utility tariff to the fogs.

In the system model, each region has a Nnumber of clusters of residential buildings. Each

cluster has nbuildings, B={b1,b2,..., bn} and each building has mSHs, H={h1,h2, ...,hm}.

It is assumed that the production and current status of MGs are shared with the fog. The fogs

have utility tariff and information of MGs to process the energy demand requests. In this system

model, PV, Wind Turbine (WT) and Fuel Cell (FC) are assumed for energy producing sources

and battery storage systems are used to store the energy in MGs. It is assumed that fogs0servers

or data centers are placed near the end-users to reduce the end-to-end latency. Moreover, when

huge requests congestion is created on fogs then the requests can be routed on the cloud for

processing. The communication between end-devices and the fogs take place through wired

and wireless communication medium like Wi-Fi, Z-Wave or ZigBee.

The end-devices generate requests and send to the fogs for processing. Each request contains

information of previously load consumption, current load demand, the source of energy (utility

or MG), cost and time (for request generation, which is sent to the fog and receive back). The

request can have other information like number of appliances, power ratings of appliances, the

current cost for consumed energy depending on the consumers services and facilities from the

fogs. Every cluster generates a huge number of requests and sends to the fog for processing. The

optimization of resource utilization is performed on the fog. Two necessary and effective steps

are; selection of efﬁcient server or data center where trafﬁc of requests are routed for processing

and balanced load requests allocation of routed requests on the VMs in the server. The necessary

information of these requests is sent to the cloud for permanent storage and to be used in future

for statistics and projects. The fogs also contain the information of MGs (production, capacity,

the rate of energy ﬂow out, etc.). When a MG has insufﬁcient power to fulﬁll the demand then

fog requests the cloud to facilitate energy consumer.

In end-user layer, the SMs of SHs in residential buildings have information of smart appli-

ances. The set of smart appliances, Sa p ={ap1,ap2, ...,a pp} in a SH are connected with the SM.

The SMs share the request with the fog and the request carries the information of Sap and own-

ership of SH. Hence, information of each appliance and biodata of the consumer are sensitive

Service Providers

Cloud

Utility

Fog 1 Fog 2 Fog N

Core Cloud Layer

Middle-ware Layer

End-User Layer

MG-1 MG-2 MG-N

Cluster-1

Cluster-2

Cluster-N

Two-way

Communication

Power Supply

Cloud Storage

Fog Server Cloud Servers

Figure 2. 2nd Proposed System Model: Rigid Community (Single Building-Single

Fog)

and called private data of the SH. However, it is assumed that the total power consumption of

a SH is shareable and called public data. The SMs categorize the energy data of each SH into

private and public.

• Private data is usually never shared; however, the companies use this information for their

statistic and analysis for future or upgrade the existing system. Such sensitive data can be

compromised in the cloud based system. In the fog based system, the information is local

and it is less likely to be compromised.

• Public data refers to total energy consumption from a building, power generation of each

MG and utility tariff. This information does not contain sensitive data and there is no risk

of sharing data.

The fog in fog-layer receives requests for energy demand from the cluster of residential

buildings and the current status of the MG. On the basis of this information, fog decides either

energy is supplied from MG or the utility. The cluster does not directly communicate with the

MG and utility. Similarly, in case of utility, the fog requests the cloud, which facilitates the

energy demand request from the utility.

4.2.1 Problem Formulation

Effective task scheduling can be done in such a way that the upcoming requests from end-

users get minimum execution time. Let ‘x’ be the number of requests received by task handler

with task length TL. The set of independent tasks is represented by Tt ask ={t1,t2,...,tx}. Every

independent task is assigned to a VM with processor speed P

s, bandwidth bw, memory Mem

and the number of CPUs Cpu. Let, V M ={vm1,vm2, ..., vmy}is the set of yVMs in the fog. The

VMs execute the xtasks in parallel. Each VM runs on its own resources and processes the tasks

independently.

The maximum completion time required for a task is the makespan of the task. The objective

of load requests balancing is to mitigate the makespan and RT. The makespan of rtask on V Mi

is denoted by CTr,i. The makespan for ron V Miis elaborated using following equation 10:

MakeS panr=Max(CTr,i).(10)

Where rεTtask,Tt ask ={1,2,3,..., r, ...,x}and sεV M ,V M ={1,2,3,...s..., y}. Mapping of

the task Ttask to yVMs affects performance parameters. Now, the total tasks assigned to each

VM are dependent on the set of end-users0requests and on the performance of load balancing

algorithm. The PT and RT of tasks are formulated using linear programming. The PT of the

allocated task Trto sVM is PTr,sand status of the task is αr,s. Total PTTof xtasks allocate to y

VMs:

α=I f t ask x is asssigned,1,

otherwise,0.(11)

The objective is to minimize PT,

PTT=Σx

i=1Σy

j=1(PTi,j∗αi,j),(12)

where,

PTi,j=TL(i)

s(j).(13)

Where, TL(i)is the length of task iand P

s(j)is the processing speed of VM j. RT is the total

time taken from sender to data center, PT in data center (by VMs) and time to receive back from

data center. It is computed with the help of Eq. (14),

RTi,j=CTi,j+TD(i) + NTD(i),(14)

Where, RTi,jis the RT for the task iassigned to VM j,TD(i)is the transfer delay and NTD(i)

is the network delay for task i.

The VM is installed on physical machine in a data center, acquiring resources to process

the tasks. There are two types of cost; ﬁxed and recurring cost. The cost of physical machine,

maintenance and installation refers to ﬁxed cost. The recurring cost is associates with the use

of resources of physical machine. Optimized utilization of resources reduce the recurring cost.

Average ﬁxed cost of a VM can be calculated with Eq. 15 when ynumber of VMs are installed

on a physical machine,

V Mc

f ixed =Pc+Mc

y,(15)

where, Pcand Mcare cost of physical machine and maintenance. The recurring cost is calculated

on the bases of number of requests arrive in the VM and the computational efﬁciency of VM.

The length of a task TLis directly proportional to the number of instruction in the task IN,

TL∝IN.(16)

The cost of a VM is calculated by the number of instructions executed in a given time. Cost for

a VM is deﬁned by execution of a Million of Instructions Per Second (MIPS). Cost of VM for

given task of length TLis,

V Mc

TL= (IN×CostMIPS ).(17)

Where, V Mc

TLis a cost cof VM for a task Twith length L. The CostMIPS is the unit cost for

MIPS. The total cost of VM for all xtasks,

CostVM =Σx

TL=iVM c

TL.(18)

The MG is placed near the fog, in middle layer, for cheap power generations; from WT (GW T ),

PV (GPV ) and stored in FC (GFC) with the costs of CostWT ,CostPV and CostFC , respectively.

The PrMG is the unit price for energy produced from the MG, which depends on the types of

RESs based power generators. The energy generated by WT and PV depend on the weather

conditions. The power generation from these resources are intermittent which also effects the

recurring their energy generation cost. FC is used for backup to keep MG alive. The capacity

of power generation of MG CapMG is the sum of power generation by RESs like GW T ,GPV and

GFC ,

CapMG =GW T +GPV +GFC .(19)

Total cost of energy produced by MG is calculated using Eq. 20. Where PrMG is the unit price

of power generating in MG, in aggregation of all types and sizes of RESs.

CostMG =Ca pM G ×PrMG .(20)

Cost of DT is the composite of interconnect and transit costs. Interconnect is a ﬁxed cost

used for connectivity with Internet Service Provider (ISP) cross linking. Transit cost T RCis a

variable depending upon bits-per-second or bytes-per-second. The cost of DT (DTC) is com-

puted by transit rate times the data size Datasz need to be transfered. The DT is calculated using

Eq. 21,

DTC=T RC×Datasz.(21)

The total cost of the system is the sum of VMs, MG and DT cost, as given in Eq. 22,

Costsys =CostVM +CostMG +CostDT .(22)

Where Costsys,CostV M ,CostMG and CostDT represents the total system cost, VM cost, MG cost

and DT cost. The objective is to minimize Costsys by optimizing PT and RT of xtasks on yVMs.

4.2.2 Load Balancing Algorithms

Efﬁcient resource utilization enhances the performance of computing resource. The cloud and

fog have huge resources to execute a huge number of requests. The resources are shared to

enhance the performance. In the synopsis, resources of a fog are shared by creating VMs.

The requests are executed according to the performance of VMs. However, execution of the

overall load of requests is optimized by the efﬁcient allocation of the requests to the VMs. The

performance of a data center is compromised when some VMs have overloaded requests and

some have very few requests or they are idle. To resolve this issue, load balancing algorithms

are used to allocate balanced load of requests on the VMs. The mechanism of load balancing is

illustrated in Figure 3. From top-to-bottom, the end-users generate requests, which are sent to

the fog data centers. The data center controller routes the requests on the potential server where

the load balancer (load balancing algorithm) efﬁciently allocates the load of requests on VMs.

The VM manager furnishes the VMs for the load of requests to execute. The VMs are created

on physical resources, which are maintained by VM monitor. However, collective performance

of these VMs depends on the load of requests assigned to them. The PT, RT and the execution

cost with proposed load balancing algorithm PSO-SA are analyzed by comparing with Round

Robin (RR), throttled and PSO algorithms.

Users

Data Center Controller

Load Balancers

(Applying Algorithms)

Virtual Machine Manager

VM VM VM VM VM VM VM VM

Physical

Server

Physical

Server Physical

Server

VM Monitor VM Monitor VM Monitor

Figure 3. Load balancing mechanism in data centers

RR Algorithm

The RR algorithm is based on equal time slicing. The RR algorithm is used to allocate the

resources by equal time slicing for efﬁcient utilization of resources. This algorithm is used to

balance the load of requests on the VMs by assigning equal time slice. The basic steps are

described in Algorithm 7.

Algorithm 7 RR based resource allocation

1: Input: List of tasks, List of the VMs

2: Output: VmId where tasks will be assigned

3: Initialize VmId=1, maxIter=maxValue

4: for vm=1; vm ≤length (VmList); vm++ do

5: Find N_tasks allocated to vm

6: if VmId=1 then

7: cur_count=0;

8: else

9: cur_count=vm.getCurAssign()

10: end if

11: ﬁnd (cur_VmState)

12: State=VmState

13: if (cur_count ≤MaxCount &&

14: State.equals (available)) then

15: MaxCount=cur_count;

16: VmId=vm;

17: end if

18: end for

19: Return VmId

The Proposed PSO-SA Algorithm

In the proposed algorithm, PSO is used to ﬁnd local best position (Lbest ) and global best

position (Gbest ) with Simulated Annealing (SA). In PSO, the best solution Gbest is found by a

sequential ﬂow of information. The best solution sends information to nearby particles. In lo-

cal search, each particle is compared to its neighbors to ﬁnd Lbest particle. However, in global

search, all particles are compared with a single best solution which compromises the Gbest per-

formance of PSO. On the other hand, the SA algorithm is inspired by the natural process of

metallurgy. SA converge to global best search after long cooling process. Hence, during heating

and long cooling process, the performance of Lbest suffers. The hybridization of Lbest of PSO

and Gbest of SA provide efﬁcient solution. The basic steps of the proposed hybrid PSO-SA are

illustrated in Algorithm 8.

PSO Algorithm

PSO is inspired from the motion behavior of birds or ﬁsh to improve the candidate solution.

The individual particle (animal) is not intelligent enough to solve complex problem. The swarm

intelligence (all the animals in search space) provides the efﬁcient solution. Each particle is

inﬂuenced by its neighbors and each particle updates itself with respects to its neighbor, which

make the whole swarm update. This leads the whole swarm to the best solution. The PSO algo-

rithm uses following steps to ﬁnd best solution.

•The particles (swarm) move in the search space by making calculations.

•The movement is made with two constraints: locally best position and best position in whole

swarm.

•Any update in a position will update the whole particle swarm, which makes the PSO inefﬁ-

cient for its global search.

The basic steps of the algorithm are given in Algorithm 9.

Algorithm 8 PSO-SA based resource allocation

1: Input: Load of requests, list of VMs

2: Initialize Population (all VMs)

3: Initialize the Best known VM Bkfor the requests

4: for Number of all VMs do

5: Input Load of Requests LR

6: if (Number of Bk==0 && LR)>1then

7: Select random VM

8: end if

9: if LR < VM (capacity) then

10: Update the the population according to Bk

11: end if

12: for x=1 to Population do

13: T=x/Bk

14: NewV M =Pick random neighbor of Bk

15: if Acceptance-Probability P(NewV M ,CV M ), T)>=rand(0,1) then

16: CVM =NewVM ; Assign current VM with NewV M

17: end if

18: end for

19: end for

20: Return list of best VM

Algorithm 9 PSO based resource allocation

Input: List of tasks/requests, List of Population Pop V Ms

Output: P

gbest

gbest =0

for c=1 to Pop do

Particle velocity P

vel =Rand(0−1)

Particle position Ppsn =Rand(Popsize )

Pp_best =Ppsn

if Cost(Pp_best )<=P

g_best then

g_best =Pp_best

end if

Stopping-condition

for (dod=1 to Popsize)

vel =Update(P

vel ,P

g_best ,Pp_best )

Ppsn =Update(Ppsn,P

vel )

if Cost(Cost(Ppsn )<=Cost(P

g_best )then

g_best =Ppsn

if Cost(Cost(P

g_best ))<=Cost(Ppsn )then

Ppsn =P

g_best

end if

end for

Return P

g_best VM

Throttled Algorithm

In the throttled algorithm, “throttledVMLoad” balancer maintains the table by indexing the

all VMs, which are available at the beginning. Data center controller receives new requests

Algorithm 10 Throttled based resource allocation

1: Input: List of tasks, List of the VMs

2: Output: VmId where tasks will be assigned

3: Throttled Load Balancer initialize an index

4: VmList checking ();

5: VmState ();

6: DC controller receives request

7: NextAllocation ();

8: CheckAvailability ();

9: if (VmId=1) then

10: Return VmId;

11: waitforNextAllocation ();

12: ModifyIndex ();

13: else

14: Return VmId=-1;

15: end if

16: if (more requests are in queue) then

17: Repeat step 8 to 15

18: end if

19: Return VM with load

from end-users. The requests query the controller where to be assigned. The algorithm 10

assigns the requests to the VMs by identifying the tags assigned by the data center controller.

If identical tags are found, then requests are considered for next allocation after amending the

tag information. The table is also updated according to the amendments. If there is no next

request then balancing function returns “-1”. The basic steps of the Algorithm are explained in

Algorithm

4.2.3 The Proposed SBP

The SBP selects the data center where requests are routed for processing. The requests from

the end-user layer are sent to the fog where SBP selects efﬁcient data center (according to the

policy algorithm) [104]. The requests query the service broker for the destination. In this work,

a hybrid of ORT and Service Proximity (SP) is proposed for selection of an efﬁcient data center.

In both SBPs index tables are maintained for available data centers. In the table of SP, the table is

indexed according to the nearest data centers. The regions are ordered in the list by the sender0s

region and region queried. The remaining are ordered in lowest latency ﬁrst. The earliest data

center (lowest latency) is picked from the list. If there are more than one data centers, then a

random data center is selected. However, the table of ORT is indexed according to RT from

the data centers using the Internet characteristics. The requests query the closest destination

(according to latency) using the service broker algorithm. The best RT is found by iterating

through each data center due to:

• Last performed task using Internet characteristics.

• If time is recorded before the threshold time, then PT is appended ‘00. It deﬁnes the idle

state of the data center.

• If closest data center does not have estimated smallest RT then on 50:50 chance either of

them is selected.

In proposed hybrid SBP, following points are followed:

• A table is maintained with index of all available data centers.

• The requests query the data center controller about the destination.

• The sender0s region and queries for the regions are enlisted using SP.

• The network delay for the regions are enlisted from the Internet characteristics and by

querying the last recorded PT (using ORT step).

• The network delay for other regions from the given region also enlisted (using SP step).

• The CDC with minimum RT is selected; otherwise, data center with least RT is selected

(using ORT and SP steps).

4.3 3rd Proposed System Model: Flexible Community (Multiple Buildings-Multiple

Fogs)

In this research work, a system model is proposed for ﬂexible community for energy manage-

ment. A cloud-fog based model is proposed in order to manage the consumers’ load requests

regarding electricity consumption. Similar, system models have also have been proposed by

[167] and [168]. The simulator, proposed for this work, divides the world into geographical

regions. It is assumed that these regions are residential which communicate with fogs for elec-

tricity load management based on users0requirements. Fogs communicate with cloud and MGs

for storing the data permanently on cloud and trigger electricity supply from MGs according to

users0requests.

Every cluster of residential buildings in the regions generate a huge number of requests and

are sent to the fog for execution. In this work, every building has 100 SHs which generate a

number of requests. Each region has two fogs forming three tier model as shown in Figure 4.

In the ﬁrst tier, each building has a controller to communicate with fog and it also manages

the power supply for the homes in the building. In the second tier, requests of end-users from

buildings are computed on fog and responded back. In the third tier, the data from fogs are

sent to the cloud for permanent storage and future use. Cloud receives data from all the fogs

and provides centralized data storage and necessary processing. The stored data can be used for

future statistical analysis and prediction based projects.

The clusters of buildings are directly connected with the fogs. The range of buildings in

a cluster is 50 to 100 and each building have 50 to 80 apartments. If there are nnumber of

buildings in a cluster then each cluster has set of buildings, C={B1,B2, ..., Bn}. The set of m

apartments in a building xis: Bx={Ap1,A p2, ...Apm}. Fog receives data from a cluster where

private data of every apartment of each building needs to be secured. Each building is equipped

with a controller which encodes the identity of apartments and appliances of it. The controller

also manages the demand and supply from and to the buildings by communicating with the fog

nodes in a region. Each region has multiple MGs which are equipped with a distributed energy

source like PV arrays, WT, Micro-Turbines (MT) and also a diesel engine for backup. If there

are kMGs in a region then set of MGs are MG ={MG1,MG2, ...,MGk}.

The requests are sent to the nearer fog node from the controller of a building for electricity

demand of each apartment. The fog node decides either demand should be fulﬁlled by MG or

utility depending on cost constraints. In case of MG, a nearest one to the building is instructed

to fulﬁll electricity demand. In case of macrogrid, fog forwards the requests to cloud which

instructs the utility to assist MG to fulﬁll the electricity demand. End users use web based

applications to control IoT based smart appliances in the apartments.

In this synopsis, a system model is proposed and will be implemented in the simulator, in

which the world is divided into six regions and fog nodes are placed in each region. Each region

has two clusters which are connected with two fogs to process the requests of each building with

Cloud

Fog 1

Fog 2

MG-1 MG-5

Fog 11

Fog 12

C11

C12

MG-1 MG-5

Service Providers

Tier 1

Tier 2

Utility

Tier 3

Power Supply

Two-way

Communicaiton

Fog Server Cloud Data

Center

Figure 4. 3rd Proposed System Model: Flexible Community (Multiple Buildings-

Multiple Fogs)

apartments. The fog node has features as shown in Table 5. End users0requests are processed at

fog which instruct the MGs or utility via cloud to fulﬁll the energy demands. The fog decides

to select appropriate MG or make macrogrid to facilitate the requests of building. In the model,

the set of ffogs is, F={F1,F2,...,Fn}and set of rregions where these fogs are placed are:

R={R1,R2,..., Rr}. Data is stored temporary on the fog and later, it is sent to the cloud for

permanent storage.

Table 5 Features of the fog

Resources Parameter Value

Image Size 10000 MB

Memory 512 MB

Bandwidth 1000 Mbps

Fog

Architecture X86

Operating System Linux

VM Manager Xen

Number of Machines 20

Machines

Memory 2048 MB

Storage 100 GB

Available BW 10000

Number of Processors 4

Processor Speed 100 MIPS

VM Policy Time Shared

4.3.1 Problem Formulation

The consumers generate requests, which are processed in the fog. The efﬁcient resource uti-

lization of fog computing minimizes the RT, PT and the computing cost. The RT and PT of

consumers’ requests are calculated as these have been formulated in the section 4.2.1. The RT

and PT are calculated using Eq. 13 and Eq. 14. The computing cost of VM is calculated using

Eq. 17 and 18. The cost of a VM also depends on the storage, memory and bandwidth assigned

to it. The total storage Stt, memory Memtand bandwidth V M Btfor the VM depends on unit

cost of them; Stuc,Memuc and V M Buc, respectively. The cost of VM in the proposed system

is calculated using Eq. 23. The cost of DT is calculated using Eq. 21. The MGs used in the

proposed system model have same RESs, hence recurring cost of the MGs are calculated using

Eq. 20. The system operating cost is the sum of VM, MG and DT cost, calculated using Eq. 22.

V M_Cost =Stuc ×Stt+Memuc ×Memt+V MBuc ×V M Bt,(23)

4.3.2 The Proposed MHBC Algorithm

The cloud and fog have huge physical computing resources, which are shared by creating virtual

resources on them. The VMs are created to process the requests of a variety of applications or

to process the requests for cross platforms or to process an extensively huge number of hetero-

geneous requests. VMs share the physical resources for efﬁcient utilization. However, VMs

themselves need to have a balanced load of tasks or requests for efﬁcient processing. The au-

tonomous load balancing algorithm is efﬁcient which neither overloads nor underloads any VM.

Heuristic and static algorithms are proposed for load balancing over VMs [102]-[103]; however,

for this research work the MHBC is proposed.

The VMs are installed on servers of cloud or fog data centers. The capacities of data centers

are different due to speciﬁcations of physical components and current status of load or trafﬁc of

requests. A SBP selects the potential data center (or server) to route the trafﬁc of requests on

it. Constraints and threshold values of parameters are used to deﬁne the policy for selection of

data center. For example, ORT is proposed, which selects the data center with minimum RT to

route the requests on it. When requests arrive at the data centers the load balancing algorithm

allocates VMs to process the requests.

Honey bees are insects which live in colonies under the command of a queen. The colony

consists of larvae, pupae, developing eggs, male drones and female worker bees. The diversity

and survival of the population depend on seasonal changes and rules deﬁned by the queen.

Algorithm 11 MHBC Algorithm

1: Start

2: Initialize Drones Population:P

V M

3: Enlist all VMs on the fog

4: Initialize the status of current VMs on the fog

5: List of Fitness of VMs on Fogs

6: Set probability ﬁtness P

7: Initialize cut-off = 8

8: Initialize Scout Population PsVM

9: for t = 1:24 do

10: for (iter ← −1 to (PsV M))do

11: ListPsV M ←PsV M ()

12: end for

13: while PsVM > -1 do

14: PathFS(i)←Probabil it yO f Fog(i): Routing on Food Source FS

15: PathFS : Routing Requests

16: until Scout Bee ==-1

17: end while

18: return PathFS: Requests on it h fog

19: for Regional search (local search from PsV M )do

20: MinCostVM (i)=MinCostPsVM :Minimum cost from Scout VMs

21: end for

22: Fit nessV M ←MinCostVM (i): Bee drone waggle dance

23: for Overall regional search (global search) do

24: MinCostVM (i)←Overall(MinCostVM (i))

25: end for

26: if V MFitness ≤P

V M (j): jth from P

V M then

27: Set V MFitness =P

V M

28: else if V MFit ness ≥P

V M (j): jth VM from P

V M

29: Current ﬁtted VM V MFitness assigned the request then

30: return ﬁtted VM

31: end if

32: end for

33: Return list of VMs with Load of Requests

The population is affected in cold season as well as tasks performed by the castes of bees.

When more than one queens exist they extend to form new colonies with the help of drones and

workers. The fertilization, food and wax to construct hive are provided by drones and worker

bees to build new colony [169]. Inspired from the colonial behavior of honey bees, honey bee

colony optimization algorithm is proposed. However, MHBC algorithm is proposed for load

balancing on VMs in the fog data centers. This algorithm is inspired by the foraging behavior

of honey bees. The scout bees represent the number of VMs for each fog. The remaining or

waiting tasks are updated according to the next allocated tasks assigned to VMs. This behavior

is found in bees when plenty of food source is found. The bees in the hives are updated about

location, distance from the hive and the amount of food though the waggle dance. In the dance,

motion relative to hive indicates the direction, vertical motion relative to source indicates the

position of Sun and duration of waggle is the distance. The waggle dance updates the VM

availability, status and load on VMs. The VMs allocated with load of requests with optimized

cost.

4.3.3 The SBPs

The policies are deﬁned to route the requests to the appropriate data center for efﬁcient process-

ing, response and cost. Service broker policy used by the service broker of cloud or fog to decide

the data center to which requests are routed. Hence, the selection of a data center is made by

SBP [104]. The selection of data center affects the RT, PT and cost. The SBPs; ORT, DR with

load and proposed New Dynamic Service Proximity (NDSP) are elaborated in the subsequent

headings.

ORT Policy

• It maintains the index of all available fogs or data centers located in the regions.

• Estimates the RT of available fogs in the region using proximity policy by evaluating

Internet characteristics.

• Checks the history of fog or data center and validates with the recent best RT.

• The requests coming from the clusters are routed to the fog located in the same region

with least RT.

DR with Load

• This policy maintains the index of all fogs or data centers available in the region.

• Requests are directed to destination fog or data center.

• Maintains the list of delays of communication medium (using Internet characteristics)

calculated from the requests it receives recently.

• The closest fog or data center to the cluster of a same region with best RT is selected.

• It also manages the fogs or data centers on current status basis. If current RT is better than

the best RT then it selects another fog with better RT otherwise current fog is selected.

• If there are more than one fogs on better statuses then random fog or data center is se-

lected.

• VMs are increased or decreased by creating and releasing the VMs on the basis of trafﬁc

load.

The Proposed NDSP

The proposed NDSP is a hybrid of ORT and SP. The limitations of ORT and SP are resolved

by hybridization of both policies. In ORT, a random selection of data center can choose inef-

ﬁcient data center; however, in DR with load, increase and release of VMs are decided on the

basis of requests trafﬁc load. This characteristic of DRL enhances the RT efﬁciency due to the

evaluation of trafﬁc load then creation or releasing of VMs.

• The proposed SBP is the extension of DRL and SP.

• Index of fogs or data centers in a region is maintained.

• Latency list is maintained by evaluating the characteristics of the Internet (communication

medium) of request coming from the region.

• Prediction of upcoming requests is made from the current requests load on fogs.

• New requests are planned to route using prediction.

• Closest data center or fog is selected on which new VMs are created or freed earlier, if

required.

4.4 4th Proposed System Model: Flexible Community (Multiple Buildings-Single

Fog (Hardware Scale-Up))

In the proposed system model, six geographic regions with groups of buildings are considered

for simulation, as shown in Figure 5. Each building has multiple SHs or apartments with IoT

based appliances. Every home has a smart meter with 5G enabled controller to monitor, control

the IoT based appliances and communicate with the controller of the building. Each building

communicates using the controller with fog node in the region for processing of energy requests

of SHs. Fog is connected with MG and the cloud. If demands of SHs are not fulﬁlled by

MG, then fog requests the utility via the cloud to fulﬁll the demand. Fog transmits the data of

consumers to cloud for permanent storage and future usage. The communication takes place

between home appliances and smart meter, building controller and smart meters, fog and the

controllers, MG and fog, utility and fogs, and cloud and fogs; however, power ﬂows from utility

and MG to the buildings. It is assumed that every communicating device has an IoT module to

share data over the Internet. Parameters for the group of buildings considered for the proposed

system model are given in Table 6.

Fog receives requests from the building controller for energy demands. Fog prefers MG,

due to cheaper energy as compared to utility and instructs to facilitate the required SH in the

building. If MG has insufﬁcient power, then it responds to the fog accordingly. The fog re-

quests the utility via the cloud to meet the energy demand of the building. The SHs generate

frequent energy requests and send to respective fog node in the region for energy demands.

When hundreds of SHs generate a number of requests for a day then computational perfor-

mance becomes challenging. High Processing Fogs (HPFs) are introduced between cloud and

buildings (end-users). HPF, unlike cloud, has low latency, lesser PT and RT with data security.

The communicating modules in the system model are monitored and controlled using IoT based

applications over web services.

Fog is like a small cloud; however, it has limited resources as compared to the cloud. It

is placed close to the end devices (end-users) and extends the cloud services on the network

edge with high performance and real-time responses. The resource sharing techniques are used

for efﬁcient resource utilization in cloud and fog computing. VMs are installed on the fog for

resource sharing to efﬁciently process the huge number of energy requests. Load of requests

on VMs effect the performance of fogs, hence load optimizing algorithms are used. The First

Come First Serve (FCFS) and ACO algorithms shall be implemented for load balancing over

VMs. However, selection of potential data center on fog node is made by SBPs; ORTP. The

simulations are performed with the simulator “CloudAnalyst” [170], in which calibration of fog

parameters are tuned, as shown in Table 7.

VMs are the programs that mimic the operations of physical machines. Resources of phys-

ical computing machines are divided logically to create VMs. Hence, physical resources are

shared using VMs to enhance the performance and unbound the speciﬁcations of resources.

However, too many VMs on physical resources limit the capacity of the resources; hence, it is

assumed that overall performance of the system should not be compromised. The system model

is proposed for SG applications, in which a huge number of requests are generated in a day for

every hour from residential buildings and are sent to the HPFs. The load of requests is routed

on data servers for RT optimization as well as on VMs which are allocated with balanced load

of requests to reduce the PT. Three scenarios are implemented with the same speciﬁcation of

hardware; however, VMs are doubled in scenario 2 as compared to scenario 1 and doubled in

scenario 3 as compared to scenario 2.

TV Coffee Maker Dryer

Washer Fridge Video

Game

IOT Appliances

HPF-1 HPF-6

MG-1

Utility

TV Coffee Maker Dryer

Washer Fridge Video

Game

IOT Appliances

MG-6

R1 R6

Each apartment

in each building

has IOT based

appliances

Consumers send request

Energy supply

request

Insufficient

energy in MG

Cloud

Power supply

Pricing

signals

Data center

Energy management response

Request

supply

Figure 5. 4th Proposed System Model: Flexible Community (Multiple Buildings-

Single Fog

MGs are equipped with RESs, which are placed closer to consumers to save power losses.

Five MGs, as shown in Equation (24), are taken with a battery storage system, RESs: photo-

voltaic arrays and wind turbines. MGs are preferred to use when the utility has on-peak hours,

while, during off-peak hours, RESs store the energy. The set of regions (R) with a set of build-

ings (GB) are given in Equations (25) and (26):

MG =MG1,MG2, ...,MGn,where n =5,(24)

R=R1,R2,..., Ri,where i =6,(25)

GB =GB1,GB2,..., GBj,where j =6.(26)

Table 6 Parameters for groups of buildings

Group of

Buildings Region Number of

Buildings

Requests/

Building/ hour

Data Size/

Request

(bytes)

Number of

MGs Avail-

able

G1 R1 100 100 128 3

G2 R2 90 70 128 4

G3 R3 72 66 128 5

G4 R4 59 59 128 2

G5 R5 86 80 128 4

G6 R6 70 60 128 2

In the proposed scenario, each region has one group of buildings with SHs or apartments.

On average, 1000 are off-peak users and 100 are on-peak users. Cost of energy consumption

is reduced in off-peak hours—this is why most users are off-peak consumers. Each region has

an HPF computing node with memory, storage, number of processors, operating system and

VM manager, etc. HPFs store data temporary and transfer to the cloud for permanent storage.

Parameters of HPFs are given in Table 7. A huge number of requests are processed on HPFs;

however, an efﬁcient load of requests over VMs and selection of potential data centers reduces

PT and RT for the consumers’ requests. Moreover, the groups of buildings generate peak-load

of requests in a day. The peak hours of “G1”, “G2”, “G3”, “G4”, “G5” and “G6” are during

4:00 p.m. to 11:00 p.m., 8:00 a.m. to 5:00 p.m., 7:00 p.m. to 11:00 p.m., 6:00 p.m. to 10:00

p.m., 3:00 a.m. to 11:00 a.m. and 3:00 a.m. to 11:00 a.m., respectively. Peak load of requests

also affects the performance and overall computation cost, which ultimately electricity users

have to pay. The tuning of parameters given in Table 6 for the load of requests of a day will be

implemented in “CloudAnalyst” for three scenarios.

Table 7 Input parameters for HPF

Parameter Value

Image Size 10,000 MB

Memory 512 & 1024 MB

Bandwidth 1000 Mbps

HPF

Architecture X86

Operating System Linux

VM Manager Xen

Number of Machines 20

Machines

Memory 2048 MB

Storage 100 GB

Available BW 10,000

Number of Processors 4

Processor Speed 100 MIPS

VM Policy Time Shared

Homes Grouping Factor 1000

Requests Grouping Factor 100

Executable Instruction

Length 100

4.4.1 Problem Formulation

In the proposed system model, energy consumers generate the requests, which are computed on

the fog like in section 4.2. The PT and RT are calculated using equations; Eq. 13 and Eq. 14.

The computing cost of requests is calculated by calculating the VM cost; Eq. 23. The recurring

cost of the system is the sum of DT, MG and VM cost. The cost of DT is calculated using Eq.

21 and the cost of MG are calculated by Eq. 20. The operating cost for the system is calculated

by Eq. 22.

4.4.2 The SBP for the Proposed System

The SBP selects potential data center for efﬁcient processing and RT. If there are multiple fog

nodes, each with the single data center then they are accessible for a user. The SBP selects a

potential fog node (fog data center) to route the trafﬁc of requests on it. The policies are deﬁned

considering a variety of parameters like PT, RT, resource availability, etc. to select the data

center where requests are routed [170]. The ORT policy is used to select potential data center in

fog node. The steps for ORT are elaborated in the section 4.3.3.

4.4.3 FCFS VM Load Balancing Algorithm

In the FCFS algorithm, the order in which requests arrive on the data center is allocated to VMs,

accordingly. When huge requests arrive on data center, then these are put in wait in a pool in

the same order as they are received. Each request is assigned with time or incremental priority

tag. The algorithm picks the ﬁrst request by calculating tag associated with it and prefers the

earliest request to feed into the system for processing. The system has VMs which receive these

requests for processing on FCFS bases. The algorithm has steps given in Algorithm 12.

Algorithm 12 FCFS

1: Input the requests along with their burst time (bt)

2: Find waiting time (wt)for all requests.

3: The request comes ﬁrst, need not to wait so, waiting time

4: Request 1 will be 0

5: wt[0] = 0

6: Find waiting time for all other requests i.e.; for Request = i + 1do

7: wt(i) = bt(i−1) + wt(i−1)

8: Find turnaround time (Tt)for all requests

9: Tt(i) = wt(i) + bt(i)

10: Find average waiting time

11: Total_wt(i)/No.o f Processes

12: Similarly, ﬁnd average turnaround time

13: Total_Tt(i)/No.o f Processes

4.4.4 ACO VM Load Balancing Algorithm

The ACO algorithm is inspired by the behavior of ants when they search for food. Ants have

no eyes and use pheromones chemical for their route to search the food. When plenty of food

is found, then others are directed to the same destination from the nest with multiple paths.

Initially, ants have multiple paths; however, after some time, the shortest path is chosen us-

ing swarm intelligence. Ants follow pheromones, which can be evaporated and the chance of

availability from one location to other is also reduced. Hence, the performance of ﬁnding or

calculating the shortest path is effected. Availability of pheromone is probabilistic; hence, ACO

is a probabilistic meta-heuristic problem solving technique. The optimized solution inspiring

from the behavior of ants is calculated with Eq. (27):

i j =(τi j (t))α×(ηi j(t))β

∑s∈Jd(i)(τis(t))α×(ηis(t))β,(27)

where pd

i j is the probability of pheromone, τi j(t)between iand jants at time t.τand ηi j(t)are

heuristic factors with coefﬁcients of αand β. The ACO algorithm has steps given in Algorithm

13.

The time required to allocate an available number of requests on the VMs is called time

complexity of ACO. The time complexity from one pheromone’s position to other is O(n)for n

positions. For iteratively visiting all positions, the time complexity is O(n2). This exponential

increase in time is too high for large instances. Hence, the PT and RT for ACO are higher as

compared to FCFS in Scenario 1. The time complexity differences in Scenario 2 and Scenario 3

are higher as compared to Scenario 1 due to a higher number of VMs. Local search is imple-

mented in each fog computing for VM allocation; hence, the space complexity is linear Ω(n)

while a global solution of ACO has space complexity of Ω(n2). The space and time complexity

of ACO are very high.

In this work, requests are the food and VMs are ants while HEMC acts as a nest of ants

and generates requests and sends to HPF using 5G technology. The potential VM is selected by

evaluating the availability and capacity for every request. Hence, before allocating the VM, the

probability and suitability of VMs are updated in the list. Thus, iterative update and prioritiza-

tion increase the time for manipulation.

Algorithm 13 ACO

1: ACO parameters initialization: pheromone, routes, iteration

2: Solution = 0

3: Iteration = 1

4: Probability of allocation of VM computed using Equation (27)

5: Random allocation of tasks to VMs

6: The value of VM is inserted into list of VMs

7: Repeat the step until the completion of tour of ants

8: Compute the Solution

9: Calculate the distance between source to destination

10: Update the pheromone

11: Again calculate the distance

12: For every edge update the pheromone locally

13: Optimal solution is replaced by current solution

14: Continue the process until best optimal solution

15: Display the results

The high time and space complexity of ACO makes it unsuitable for huge data (e.g.; more

than 3800 data requests [171]). For huge data, ACO traps in local optima. However, the number

of requests or data are small and do not trap in local optima. However, time complexity is very

high as compared to FCFS. It is assumed that probability of pheromone evaporation is reduced.

Moreover, co-efﬁcients αand βare reduced to control local optima of ACO.

4.5 5th Proposed System Model: Rigid Community (Power Economy Sharing)

In this subsection, a three layered system model is proposed for rigid community in realistic

scenario, as shown in Figure 6. In the lowest layer, there are nnumber of smart communities.

Each community has the hnumber of SHs. Each SH has BESS for uninterpretable and cheap

power supply. The energy is stored during off-peak hours and consumed during on-peak hours to

save maximum cost. However, these BESSs of the community are interconnected and controlled

from the fog in the middle layer. The interconnection of BESSs forms a UESS, which fulﬁll

the demands of SHs in the community instead of shifting them on the utility. The decision of

shifting a SH from utility to UESS or UESS to utility is made on the fog. Each community has

a fog in the middle layer to serve as-a-Power-Economy-Sharing. These fogs are connected to

a cloud on the top layer. The cloud provide storage-as-service and broadcast the utility tariff to

the fogs in the middle layer. The data from fogs are moved to the cloud for permanent storage

to be used in future for statistical analysis and forthcoming projects.

Cloud Utility

1. Price Signals from Utility

2. Permanent Storage for future

Unified-

ESS Smart

Community

Community 1

Unified-

ESS Smart

Community

Community 2

Unified-

ESS Smart

Community

Community n

Fog 1 Fog 2 Fog n

1. Switching: ESS and Utility

2. Smart Billing

Power Supply

1. Switching: ESS and Utility

2. Smart Billing

1. Switching: ESS and Utility

2. Smart Billing

Figure 6. 5t h Proposed System Model: Rigid Community (Power Economy Shar-

ing)

There are ten SHs in each community which communicates with their respective fogs for

energy management and share the information of BESSs to participate in UESS. A community

consists of 10 SHs which communicate with the fog for energy management. Energy distri-

bution of UESS within the community is performed in the fog layer which serves as-a-Power-

Economy-Sharing. Four agents are proposed; Home Agent (HA) is responsible for energy man-

agement at SH level and share the necessary information with the fog, Energy Storage Agent

(ESA) and Power Distribution Agent (PDA) reside on the fog and control the power distribu-

tion according to the capacities of BESSs and the Cloud Agent (CA) performs two operations;

broadcasting the utility tariff to the fogs and manages the fog data for permanent storage. The

twelve smart communities are considered and will be implemented in aforementioned system

model with three scenarios.

In the ﬁrst scenario, SHs without BESS are connected with utility. In the second scenario,

SHs have BESS and also connected with the utility. The load of appliances is scheduled ac-

cording to utility tariff; however, the consumer can interrupt the scheduling for his satisfaction.

When battery based energy is full then the SH shifts power consumption from utility to BESS

until it is fully discharged. In the third scenario, appliances are scheduled according to the utility

pricing. The fogs receive information of BESSs from SHs of respective community and utility

pricing from the cloud. The information of BESSs is used to form UESS for the community.

On the basis of this information, fog generates signals for SHs to switch between the utility and

UESS.

4.5.1 Scenario-1: Communal SHs with Utility

In this scenario, SHs fulﬁll their energy demands from the utility. In order to minimize the

energy consumption cost a load of appliances is scheduled according to utility pricing. The

scheduling of a load of appliances from high to low tariff time minimizes the power consumption

cost. The heuristic techniques are used for autonomous scheduling of appliances to minimize

the energy consumption cost.

In the scenario, hnumber of SHs in each community consume power from the utility. The

utility tariff ﬂuctuates according to the consumers’ demands and characterized as on-peak and

off-peak hours. The load of a SH is shifted to off-peak hours to reduce the power consump-

tion cost; however, practically, the whole load cannot be shifted in off-peak hours. Therefore,

the total power consumption is the sum of power consumed in on-peak hours ONpand power

consumed in off-peak hours OFFp, as given in Eq. 28,

pc =ONp+OF Fp,(28)

Where Ts

pc is the total power consumption of a SH s.

Total power consumption for all SHs Th

pc in the community is calculated by Eq. 29,

pc =

∑

n=1

Tpc.(29)

The behavior of power consumption of every SH in the community is different from others. So,

the average power consumption of a SH in community is calculated with Eq. 30,

AvgPCh=∑h

1Tpc

h.(30)

Where AvgPChis average power consumption of hSHs of a community. The cost of power

consumption in given time Costt

pc is the product of power consumed PCtand tariff Trtin the

time, as shown in Eq. 31,

Costt

pc =PCt×Trt.(31)

4.5.2 Scenario-2: Communal SHs with Personal BESS

In this scenario, unlike scenario-1, SHs use BESS along with the utility. During on-peak time the

demands are fulﬁlled with BESSs; however, demands of SHs and batteries are charged during

off-peak time from the utility. Appliances are scheduled according to utility pricing signals;

however, the consumer can use any appliance at any time without considering pricing signals

because of BESS. If storage of BESS falls short then the SH consumes utility power at its tariff.

For instance, there are sSHs consume power from BESSs and the utility. The total payable bill

is the sum of power consumed from the utility at its tariff and cost of energy consumed from the

BESS. The power cost of BESS is less than the utility or has the maximum cost equal to tariff

at off-peak time of the utility. The cost of ‘s0is calculated as shown in Eq. 32,

total =∑(PCt

BESS ×E Pt

BESS ) + ∑(PCt

Ut ×T rt

Ut ),(32)

where PCt

BESS is power consumed from BESS and EPt

BESS is electricity price of BESS in

given time t. Similarly, PCt

Ut .is power consumption from utility and EPt

Ut .is the electricity

price for given time t. The power consumption of utility during on-peak hours increases the

overall cost. User comfort is achieved however, the cost is compromised when utility power is

consumed.

4.5.3 Scenario-3: Communal SHs Participating in UESS

When the storage of a SH falls short, it has one of two options: either buy energy from the

utility or from the energy storage of the neighboring SH. The utility energy is expensive as

compared to battery storage. In this scenario, the BESSs of SHs in the whole community are

uniﬁed and shared among SHs during on-peak hours or when the storage of a SH falls short.

The uniﬁcation of BESS is based on no-proﬁt-no-loss, where no SH seeks personal beneﬁts

only. Every SH has invested in their BESS according to their own demands so, every BESS

has the different capacity with a different investment. Suppose, the nnumber of SHs participate

in UESS for the communal economic beneﬁt. Appliances are scheduled according to utility

pricing signals; however, when the consumer interrupt the scheduling the system warns with a

preferred power source (UESS or utility). The pricing signals of UESS are always lesser than

the utility.

Every SH has a program called HA, which measures the demand for SH and current storage

status of BESS. The HA updates the ESA in the fog, which generates control signal for SH to

use UESS or the utility. It also maintains the communication between the communal SH and

the fog. ESA communicates with PDA for ensuring smooth power supply to the SHs of the

community. SHs of the community. The excessive stored energy of BESS is utilized in the

community rather than selling back to the utility. The agents unify the BESSs with excessive

energy and demanding SH is entertained with this energy rather than buying from the utility.

BESSs use the utility energy for storage during off-peak hours and UESS provides cheaper

energy in the community rather than the utility. The agents: HA, PDA and ESA are used to

ensure the effective energy cost for the community.

Every SH is equipped with different power rated appliances in different numbers; hence,

BESSs are installed accordingly. For this scenario, some assumptions are made:

•Installation of BESS is devised according to demand of the SH.

•Power consumption behavior of every SH is unique.

•The investment of BESS is made according to the capacity.

•Every BESS has a one-time (ﬁxed) cost.

The SHs are preferred to use UESS during on-peak hours; however, if sufﬁcient storage is

available during off-peak hours UESS is still preferred. Because, UESS has lesser or equal to

tariff of the utility during off-peak time. Total cost for the hnumber of SHs in a community is

calculated using Eq. 33,

Costn

total =

∑

i=1

(PCi

UE SS ×T rt

UE SS +PCi

ut ×Trt

ut ),(33)

where, Costn

total is total cost for nSHs, PCi

UE SS is power consumption of ith SH at tariff of

UESS Trt

UE SS for time t, the ith SH consumed. PCi

ut.is power consumed from the utility ut.by

ith SH at utility tariff Trt

ut.for tlong time.

UESS is charged during off-peak hours and is discharged during on-peak hours. PDA gen-

erates signals to shift SH from personal BESS to UESS when storage depth left with 20%. If

UESS is at lowest storage depth then SH is shifted to the utility. It is assumed that UESS should

not reach at 0% storage. For that when UESS reaches 20% remaining storage then it is consid-

ered as “0-level”. When it lefts with “10%” of storage, it is considered at “-10 level” and when

the storage is zero then it is considered as “-20 level”. So, when UESS reaches at “0 level” SHs

are shifted to utility and UESS put to charge.

It is also assumed that the investment made for every BESSs is different and according to

the demand of the SH. So, BESS of different storage capacities are used in UESS. HAs share

the information of BESSs with the ESA on the fog to form the UESS. When a SH consumes all

the energy from its BESS then ESA requests the PDA to facilitate the SH from UESS or utility

depending on current UESS storage. The pricing of UESS is not more than the tariff of utility

during off-peak hours.

4.5.4 The Fog Environment

In the system model, every community has its own fog node. In the fogs, physical computing

resources are shared by creating virtual resources [172]-[173]. The requests and number of VMs

affect the RT, PT and the cost. VMs are programs of physical machines with similar functions.

The processing speed of a VM is measured in a number of instructions processed in a unit time.

To enhance the performance, the potential data center is selected where requests are routed. The

requests are efﬁciently allocated to VMs in the data center. The RT is optimized by selecting the

potential data center or server using CDC SBP. The PT is optimized by allocating the requests on

VMs using RR algorithm. The RR assigns the time quanta to each VM to process the requests

without priority. The RT and PT are optimized using SBP and the load balancing algorithm.

However, number of VMs also affect the performance of fog, hence number of VMs are altered

to analyze the affects on the performance.

4.5.5 Mapping of Multiple Agents

In adherence to the system model a multi-agent communication model is proposed for each

community, as shown in Figure 7. In the ﬁgure “Com. 1”, “Com. 2” and “Com. n” represent

the communities. Agents are standard programs used in intelligent systems to run, maintain

and self healing the system. In the proposed system model, a communication model for the

agents is proposed for energy management. SHs in the community have HAs, which perform

three tasks; maintain storing operation during off-peak hours, the energy demand of SHs are

entertained from BESS during on-peak hours and share the status of BESS with ESA. The HA

resides between the SH and BESS and ESA resides on the fog. The ESAs form the UESS with

the help of HAs of respective community. ESA also communicates with PDA which uses smart

metering between the community and the fog for smooth power supply to SHs. ESA and PDA

also communicate with Cloud Agent (CA) for utility tariff and permanent storage of fog data on

the cloud.

HA1 HA2 HA10

Com. 1

ESA-1

PDA-1

HA1 HA2 HA10

Com. 2 Com. n

Figure 7. Multi-Agent Communication Model

4.5.6 The Cloud Environment

In the Section 1 and the Section 2 the purpose, the sizes, the computing infrastructures and the

feasibility of cloud for various kinds of problems are discussed. The cloud has virtually inﬁnite

computing resources. The resources are shared by creating virtual resources and different tech-

niques are used for efﬁcient performance. Although, the cloud has efﬁcient PT; however, RT is

higher due to a number of hops between the client and the cloud [174]. These characteristics

make the cloud feasible for delay immune applications. In the proposed system model, the cloud

is considered for Storage-as-a-Service for permanent storage of fog data. It also broadcast the

utility tariff to the fogs. The CA performs the data storage and broadcasting of the utility tariff

to the fogs.

4.5.7 Appliances Scheduling Schemes for SHs in the Communities

The SHs in the community schedule their appliances using GA, EHO and their hybrid, named as

EGO or EHO-GA. The algorithmic steps of proposed hybrid scheme are given in Algorithm 3.

In the proposed system model, the scheduling schemes are implemented according to aforemen-

tioned scenarios. In ﬁrst scenario, appliances of SHs are scheduled in consideration of utility

tariff only. Similarly, in second scenario appliances are scheduled in consideration of BESSs

and in third scenario scheduling are performed considering the UESSs.

4.6 6th Proposed System Model: Prosumer Oriented Fog based Energy Manage-

ment as a Service (Flexible Community)

In this synopsis, the proposed FEMaaS considers price ﬂuctuation for multiple time steps T,

forecast renewable energy generation, power storage and power demand factors to achieve op-

timal global cost for the smart community. Considering the factors like utility pricing and ESS

can help to avoid on-peak time steps. However, the storage more than actual future power de-

mand with RESs can degrade the system. Hence, it is important to consider forecast renewable

power generation and energy demand altogether. In the following subsections, the system model

and its operations for cost-efﬁcient energy management service for prosumers are explained.

4.6.1 Data Utilization

The sequential time-series data from different components are utilized by FEMaaS. The param-

eters used are: production capacity, the energy demand of the smart community and energy

pricing for Ttime steps. The one hour time step makes T=24 for a day. The production ca-

pacities of large Cland small Cmscale RESs depend on the environmental conditions and can

be forecast according to the geographical location. The energy demand for a smart community

Dis foreknown if consumers intimate their demand in advance or predicted from the historical

data. Each community has dedicated power lines with pre-assigned load capacities depending

on the load demand and decided by the local utility companies.

Pricing data is used to calculate the cost of power consumption. It is also used for selling

cost of energy. Puis the buying price from the utility or power grid. The prices are time-variant

and are predetermined by the utility companies depending on the environmental and power

demand conditions. The Puis broadcast to the fogs and kept in information pool to input for

management. Pru is the price when renewable energy is sold to the power grid.

Pru is considered lower than Pudue to free energy sources like wind and sunlight for power

generation as well as to facilitate the utility. The Eq. 34 shows the relationship between Pu

and Pru, where the coefﬁcient αis the regional environmental penalties and it ranges from 0

to 1. Pris the trading price of renewable energy in the community, which is based upon the

agreement between FEMaaS provider and the prosumers. The value of Pris determined with

Eq. 35, which is between the Puand Pru .βranges from 0 to 1, which is ﬁxed according to the

contract between prosumers and FEMaaS.

Pru =αPu,(34)

Pr=β(1−α)Pu+Pru.(35)

Power Grid

Fog-1/1 Fog-2/1 Fog-n

Cloud

Memory-as-a-Service

Information Sharing

Power Distribution

Small-scale Renewable Generator Storage System

Smart Building Smart Home

Conventional

Power Generators

Pru

Figure 8. 6th System Model

4.6.2 Cloud-Fog based Architecture

Cloud computing provides the computing services over the network (Internet) by sharing the

hardware and software resources. NIST deﬁnes the cloud as a model accessible via network for

on-demand, convenient and ubiquitous shared pool of customizable computing resources with

easy management or minimum interaction of service provider. Individuals and organizations

use cloud resources to reduce investment, i.e. purchase, install and maintain the computing

systems with minimum management [31], [151]. In SG, end-users access the cloud infrastruc-

ture for energy management services through Application Programming Interface (API) and

Web browsers. The cloud has virtually inﬁnite computing resources [175]-[176]; hence, it can

accommodate maximum end-users or customers for energy management services. However,

increased number of end-users and centralized cloud infrastructure increase the PT and RT [27],

[61]. Fog is like a cloud and it is also termed as cloudlet (small cloud) to provide services on the

edge of the network; i.e., close to the end-users, for low-latency and provision of real-time ser-

vices [130]. In SG, the delayed response for energy management can cause the instant demand

load-peak creation, which degrades the system efﬁciency. Therefore, in this synopsis, inspired

from [177] a cloud-fog based system model is proposed and shown in Figure 8. It is proposed

for energy management of multiple smart communities with near real-time services provision.

The smart communities are directly connected with their respective fogs for energy man-

agement services using high bandwidth wired and wireless technologies. A big community can

have access to multiple fogs to overcome the increased PT due to limited computing resources

as compared to the cloud. However, the fogs are dedicated to the community only. Each smart

community has dedicated one or more fogs (depending on size of the community). In case of

more than one fogs, an appropriate is selected by deﬁning SBP. The service broker policy is

deﬁned for prosumers’ aspect (service with minimum RT). Single fog is sufﬁcient for a small

community to provide the service. How big a fog should be to fulﬁll the requirements of a com-

munity?. The answer to this question lies in resource sharing techniques, number and sizes of

physical and virtual resources [178]-[179]. These community fogs are connected with the cloud

to store periodic information like community energy demands, the number of registered end-

users or prosumers and change in community size, etc. It is done for permanent storage to be

used for future statistical analysis and projects. Hence, the cloud provides Memory-as-a-Service

in this synopsis. Cloud-fog based infrastructre can reduce signiﬁcant energy consumption by

providing real time service. The proposed FEMaaS is an extension of the Platform-as-a-Service

model designed to achieve optimal global cost by integrating conventional and renewable en-

ergy generators. Prosumers are encouraged to integrate their ESSs and small-scale RESs to

gain maximum incentives. A large-scale renewable power generator is also integrated into the

system to reduce conventional fuel based expensive power generation and reduce the emission

of CO2. Hence, FEMaaS integrates DERs to form virtual REPs and improves the integration of

renewable energy for the optimized cost.

4.6.3 Infrastructure

In Figure 8, the cloud communicates with power grid and fogs for sharing periodic energy

prices. The cloud receives pricing signals from the power grid and broadcast to the fogs.The

sequential periodic information from the components of a smart community: large-scale RESs,

small-scale RESs, prosumers’ energy demand, surplus renewable energy, geographical data and

prices from/to the power grid makes the information pool in the fogs.bThe service managers run

these information to provide service in the community. The service manager of the fog provides

FEMaaS to suggest ideal choices for prosumers of the respective smart community.

Every community has access to two types of nondispatchable DERs; large-scale renewable

generators and small-scale renewable generators. In large-scale, energy sources are installed and

maintained by the individual company for business purposes. They own wind farms and solar

parks to generate a huge amount of power. In small-scale, the RESs are installed on the rooftop

of a building. In smart communities, photovoltaic panels are installed on the rooftop of every

residential building. The energy of these small-scale RESs is consumed for building’s demand

and storage. The surplus energy is traded with the power grid and the smart community. Hence,

these small-scale renewable energy producers prosumers (consume and trade the energy). The

prosumers are encouraged to participate in the system to reduce CO2emission from conven-

tional fossil fuel-based power generators. The prosumers are incentivised with reduced energy

cost by trading their surplus energy. The trade takes place under the signed agreement between

renewable energy producers and the local utility. There are variety of agreements available be-

tween prosumers and utility companies [180]-[183]; however, authors of [61] mention the rule

25.211 i.e. present in public utility commission of Texas. The conventional power generators

are also connected to fulﬁll the power demand; however, it is not environment-friendly due to

the emission of CO2, which causes global warming.

ESSs store cheap or free energy and consume during on-peak time steps. The storage of re-

newable energy is free and stored energy is cheap when bought from the utility during off-peak

time steps. The ESSs also cures the intermittency of renewable generators. In the proposed

infrastructure, ESSs are the components for small-scale renewable generators, which are main-

tained by the service manager of respective fog. The ESS and RES are connected in pairs with

community power lines to store energy from RESs and participate in FEMaaS.

4.6.4 Cooperative Procedure

The cooperative procedure between fog’s service manager and rest of the components take place.

The data of the components for Ttime intervals are gathered in the information pool of the fog.

The optimal global beneﬁts are determined by utilizing information with the running of the

energy management program. The decision variables are utilized to calculate the corresponding

energy cost for every prosumer. Every prosumer is suggested through API for the amount of

power selling/buying in every time step of T. The power generation from the large-scale RESs

is sold to the power grid or to the smart community. The amount of energy bought/sold by every

prosumer (Ae

x,i,t) in a smart community xis mapped in Eq. 36 and the As

x,i,tin Eq. 37 indicates

the bought or sold renewable stored energy from ESS of a prosumer from the community.

x,i,t=Bd

x,i,t+Bu

x,i,t+Bs

x,i,t−Src

x,i,t−Ssc

x,i,t−Ssu

x,i,t−Sru

x,i,t(36)

x,i,t=Bs

x,i,t−Srs

x,i,t−Ssu

x,i,t.(37)

The power demand can ﬂuctuate suddenly, which can cause forecast error. This inaccurate

unpredicted gathered data makes the service impractical to satisfy ideal demand suggestion for a

prosumer. Hence, adjustment for such error needs to be performed in the cooperative procedure

in the fog. When anomalies are observed, then deﬁcient or surplus power (γa) is bought from

the external grid at current pricing Pct or sold at αPct and differential cost (γc) is determined.

The γcis affected by forecast error (difference of Pct and Pu).

The adjustment process requires a time interval, which is calculated from the current real-

time market operation. In [184], the authors use the interval of ten minutes in the process of

economic dispatch problem. The service manager veriﬁes every prosumer for the actual amount

of energy acquired and produced in real-time during adjustment interval. The real-time data of

prosumers are obtained via APIs by the service managers to calculate ω; the ration of γcand

(Copt

x) a corresponding cost for whole community x. If the value of factor ωis greater than

threshold λ, then energy management program runs again for next time steps untill T. In other

case, decision variables continue to follow each prosumer for next adjustment interval until the

time (T−interval ) is reached.

4.6.5 Formulation for FEMaaS

The components of a smart community cooperate with FEMaaS to enhance the integration of

renewable energy with power grid supply and maximize incentives (maximum reducing global

cost). A LP model is formulated for FEMaaS to operate the huge information of a smart commu-

nity and the participants. The model is formation is inspired from [61] with decision variables

Bd,Bu,Bs,Src,Ssc,Ssu,Sru,Sl u,Sl c and state of charging and discharging variable S. However,

operation cost for RESs and ESSs are negligible and ignored.

The objective function of the LP model minimizes corresponding cost for every prosumer

Cmand every large-scale renewable generator Clduring Ttime steps. The Eq. 38 shows the

objective function where the Eq. 39 and Eq. 40 show the corresponding cost for the prosumer

and large-scale renewable energy generators, respectively.

min ∑

x∈N,t∈T

Copt

x,t=∑

x∈N,t∈T∑

i∈M

x,i,t+∑

j∈L

x,j,t,(38)

x,i,t=Bu

x,i,t×Pu

t−(Ssu

x,i,t+Slu

x,i,t)×Pru

t+ (Bd

x,i,t+Bs

x,i,t−Ssc

x,i,t−Src)×Pr

t,(39)

x,j,t=Slu

x,j,t×(−Pru

t) + Slc

x,j,t×(−Pr

t).(40)

To ensure the smooth integration of renewable energy some constraints are necessary to

deﬁne with subject to objective function to avoid conﬂicts due to physical components of the

system or the limitation of the system. To attract maximum prosumers to participate in renew-

able energy generation, they should be given equivalent chance to fulﬁll the energy demand.

The criterion is to fulﬁll the total power demand of a community with grid power and renewable

power generations. The constraint is deﬁned in Eq. 41, which guarantees the satisfaction to

fulﬁll the required demand without using load shifting algorithms and attracts the prosumers to

participate in FEMaaS.

Dx,i,t=Bd

x,i,t+Bu

x,i,t.(41)

The total sold renewable energy should be equal to the produced energy. The constraints are

given in Eq. 42 and Eq. 43. The constraints assure that sold energy to all communities is equal

to the produced energy from large and small scale RESs.

Slc

x,j,t+Slu

x,j,t=Gl

x,j,t∀n communities,(42)

Src

x,i,t+Sru

x,i,t=Gm

x,i,t∀n communities.(43)

For every time step, the choices of every prosumer changes, which makes the variations in avail-

able renewable energy for the smart community. Hence, it is important to trace the renewable

energy variations using Eq. 44 to avoid buying of power more than the available amount.

∑

j∈L

Slc

x,j,t+∑

i∈T

(Ssc

x,i,t+Src

x,i,t) = ∑

i∈MBd

x,i,t+Bs

x,i,t.(44)

It is discussed earlier that every smart community has power lines, which have power car-

rying capacities. The power load should not be more than the capacity Wz,tof the power line (z)

in a given time step (t). The assigned power load should satisfy the constraint given in Eq. 45.

∑

i∈Wz

x,i,t+∑

j∈WzSlu

j,t+Slc

j,t≤Wz,t∀z∈W.(45)

The ESS should be efﬁcient with negligible energy conversion loss. The lowest energy state

is set as 0 when the battery is left with 15% of remaining storage (to ensure the battery life).

However, the maximum storage state is set Smax . The state variable Sdepends on the state of

previous time step and curring charging or discharging decision, which is given in Eq. 46 for

community x. The sold energy from storage to power grid and community should not be greater

than state 0, which is given in Eq. 47. The storage with bought energy should not exceed the

maximum storage capacity Smax

x,iof the battery in smart community xas given in Eq. 48.

Sx,i,t+1=Si,t−Ssu

x,i,t+Ssc

x,i,t+Bs

x,i,t(46)

Sx,i,t−Ssu

x,i,t+Ssc

x,i,t≥0 (47)

Sx,i,t+Bs

x,i,t≤Smax

x,i.(48)

The FEMaaS coordinates with many DERs and power demand to form a single system and

restricts the production of renewable energy between upper and lower bounds. The upper Vup

and Vlw values are decided by the commitments to enhance the integration of renewable energy.

If the difference of Vup and Vl w is less, then the reserved capacity from convention power

generation is also less. Hence, RESs in the smart community should smoothly with the lesser

running of reserve conversational generator fog requirement. The difference of total demand

and total bought energy from the conventional power generators in every time step of Tshould

satisfy the constraint given in Eq. 49, which is the amount of renewable energy produced during

every time step for the smart community.

Vlw ≤Dtot al −"∑

i∈MBu

x,i,t−Ssu

x,i,t−∑

j∈L

Slu

j,t#≤Vup.(49)

5 Results and Discussions

The simulations have been performed for 4th proposed system model with three scenarios.

However, one of them is elaborated to analyze the efﬁciency of proposed system model.

5.1 4th Proposed System Model: Flexible Community (Multiple Buildings-Single

Fog (Hardware Scale-Up))

5.1.1 Scenario-1

In the scenario, 25 VMs are installed on HPFs and group of buildings generating requests,

which are sent to the fog computing node hourly, as shown in Figure 9. VMs are allocated to

these requests using ACO and FCFS load balancing algorithms. Every HPF entertains group of

buildings in the respective region. RT is measured in milliseconds for each group of buildings

and average, minimum and maximum RT is given in Table 8 using ACO algorithm.

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time (hrs)

Number of Requests

×104

Fog1

Fog2

Fog3

Fog4

Fog5

Fog6

Figure 9. Number of Requests Load on each HPF

The PT of each HPF with the given scenario using ACO algorithm is given in Table 9.

Average, minimum and maximum PT with 25 VMs of each HPF are also given in the table.

Similarly, for same number of requests the RT for each group of buildings has average,

minimum and maximum are given in Table 10 using FCFS algorithm. Average, minimum and

maximum PT of each HPF using FCFS for requests allocation to VMs are given in Table 11.

The PT of each HPF is given for the requests of buildings generated for a day.

Total cost of VMs, MGs and DT for ACO and FCFS algorithms are same as shown in Figure

10. Each HPF covers requests of group of buildings in each region; hence, costs of each fog

Table 8 RT of each Group of Buildings for ACO

Group of

Buildings

Average

(ms)

Minimum

(ms)

Maximum

(ms)

G1 52.31 38.82 64.63

G2 51.29 39.99 66.39

G3 52.47 40.22 66.60

G4 52.31 38.67 64.17

G5 54.66 41.24 70.15

G6 54.92 42.28 71.27

Table 9 PT of each HPF for ACO

HPF Average

(ms)

Minimum

(ms)

Maximum

(ms)

HPF1 2.59 0.04 9.82

HPF2 1.58 0.03 6.18

HPF3 2.64 0.03 9.06

HPF4 2.63 0.04 9.93

HPF5 4.79 0.08 17.15

HPF6 5.19 0.67 19.56

Table 10 RT of each Group of Buildings for FCFS

Group of

Buildings

Average

(ms)

Minimum

(ms)

Maximum

(ms)

G1 52.20 38.82 64.63

G2 51.22 39.97 66.39

G3 52.33 39.90 66.60

G4 52.19 38.67 64.17

G5 54.38 41.24 66.78

G6 54.51 42.28 67.12

Table 11 PT of each HPF for FCFS

HPF Average

(ms)

Minimum

(ms)

Maximum

(ms)

HPF1 2.47 0.04 4.08

HPF2 1.51 0.03 2.70

HPF3 2.50 0.03 4.06

HPF4 2.51 0.04 4.10

HPF5 4.51 0.08 7.25

HPF6 4.78 0.67 7.09

associated with respective MGs, VMs of HPF and DT. The total cost at “Fog4” is the least and

the reason is, number of requests are just above the average for early 18 hours and reduced to

minimum for next 3 hours reached to above average again for next 3 hours.

The scenario is also implemented with cloud computing based system. The cloud has sum

of requests of all HPFs with FCFS and ACO algorithms for allocation of requests to VMs along

with ORTP service broker policy for selection of data center. In the Table 12 RT is increased

compared with ACO and FCFS compared to individual HPFs. However, RT for groups of build-

Fog1 Fog2 Fog3 Fog4 Fog5 Fog6

100

200

300

400

500

Cost ($)

Data Transfer

Total

Figure 10. VM, MG, DT and Total Cost for ACO or FCFS

ings in all regions from cloud computing using FCFS lacks from using ACO algorithm. The PT

of cloud computing based system is reduced compared to individual HPFs. Similar, difference

is observed with minimum and maximum RT and PT using ACO and FCFS algorithms in the

table. The delay in RT can affect the cost efﬁciency of energy consumer. For instance, electricity

price rates are updated in some time at cloud which would be responded with delay to consumer.

The consumer would continue the scheduling with previous pricing which can increase the cost.

Performance of SG is a real-time based system. Implementation for cloud and cloud-fog based

SG validate the performance of HPFs based system with efﬁcient processing and permanent

storage of data on cloud for future use.

Table 12 Scenario 1:Response and Processing Time of cloud

Time Load Balancer Average (ms) Minimum (ms) Maximum (ms)

Response ACO 98.31 69.68 106.62

Processing ACO 1.12 0.06 2.1

Response FCFS 101.34 87.02 112.03

Processing FCFS 1.92 0.10 2.6

5.2 2nd Proposed System Model: Rigid Community (Single Building- Single Fog)

In this system model, extensive simulations are performed for the NSBP and load-balancing al-

gorithm. Three scenarios are implemented to analyze the performance of proposed techniques.

However, in this synopsis results of scenario-1 are discussed. The RT, PT and cost are analyzed

using CDC, ORT and proposed NSBP to route the requests on the potential data center. The

requests in data centers are allocated on VMs using RR, PSO and proposed PSO-SA. The sim-

ulations are performed for 24 hours (a day) using the CloudAnalyst tool. The load and size of

requests from the consumers, number, and size of VMs, hardware and virtual speciﬁcations of

fog computing are initialized according to [106].

5.2.1 Scenario 1: 25 VMs and Residential Buildings

In this scenario, three residential buildings from three clusters in three regions are considered.

Each building has 100 SHs which generate requests every hour to process on the fog in the

region. Each cluster is also connected with MGs for uninterrupted and cheap power supply.

The requests are routed on potential data center using ORT. The balanced load of requests is

allocated to VMs using RR, throttled, PSO and proposed PSO-SA. Simulations are performed

with each algorithm separately to analyze the performances of RT, PT, and the cost. Each fog

data center in the region has 25 VMs. The data for users for peak and off-peak hours is taken

from [107]. The simulation results for RT are discussed in the following headings.

RT for Residential Buildings

The buildings generate different number of requests, which are sent to the fog. The request

trafﬁc is routed on the data centers using ORT. ORT reduces the RT and increases the system

performance for end-users (SHs in the buildings). Four scheduling algorithms; RR, throttled,

PSO and PSO-SA are used to allocate load of requests on the VMs in the data centers. Efﬁ-

cient allocation enhances the performance for PT. Therefore, SBP and load-balancing algorithm

enhance the system performance and reduce the computational cost. The fog has limited re-

sources and its performance is compromised due to trafﬁc congestion and load of requests. The

RT is compromised during peak hours when end-users generate huge numbers of requests for

the fogs to process. Every building belongs to a different region and residents of each region

have different power consumption behavior. Hence, each building generates a different number

of requests, which are processed on the fog. The least number of requests are generated by

Building-2 and the most requests are generated by Building-1. In Building-1 and Building-3,

the RR has the highest RT. The proposed PSO-SA has the least RT. Meanwhile, throttled has

higher RT than PSO. In Building-3 during hours 2, 12, 14, 15, 16, 17 and 21, the RT using RR

and throttled are almost same and the number of requests during this time are few. Similarly,

in Building-2, the number of requests is less than Building-1 and Build ing-3. RT using RR is

smaller or equivalent to throttled. Similarly, PSO-SA also performs better with fewer number

of requests. The simulations validate the inefﬁciency of RR, throttled and PSO with high load

of requests while proposed PSO-SA is the efﬁcient. The PSO-SA has the least average RT and

RR has the highest RT. The PSO has 64.04 ms, throttled has 75.11 ms, RR has 83.00 ms and

PSO-SA has 60.77 ms average RT as given in Table 13.

Table 13 RT in Scenario 1

Algorithm Average RT (ms) Minimum RT (ms) Maximum RT (ms)

RR 83.00 42.78 99.74

Throttled 75.11 40.26 96.53

PSO 64.04 39.53 90.28

PSO-SA 60.77 37.98 290.86

PT of Requests

The RT is affected by network latency and the PT. If the average PT of PSO-SA is efﬁcient

then RR, PSO are throttled; however, PSO-SA is the most efﬁcient as given in Table 14. The

requests from all (three) buildings in respective regions are sent to the fogs for processing. As

discussed earlier, the most requests are generated by Building-1 and Building-3 and sent to

Fog-1 and Fog-3, respectively. In Fog-2 there is a lesser number of requests as compared to

Fog-1 and Fog-2 to process. The PT of PSO is more compromising than PSO-SA in Fog-

2. These hourly performances for PT in Fog-2 show that proposed PSO-SA is most efﬁcient.

The performance of RR is compromised when sizes and number of requests are big. Because,

partially executed requests sit in wait for their next turn. However, PSO-SA performs efﬁciently

for any number of requests due to efﬁcient local and global solutions.

Table 14 PT in Scenario-1

Algorithm Average PT (ms) Minimum PT (ms) Maximum PT (ms)

RR 2.37 0.21 3.99

Throttled 2.49 0.25 4.47

PSO 2.91 0.26 4.96

PSO-SA 1.14 0.17 2.30

References

[1] Aslam, S., Javaid, N., Khan, F.A., Alamri, A., Almogren, A. and Abdul, W., 2018. “Towards

Efﬁcient Energy Management a

Title of Research Proposal Towards Smart Cities: A Move for Efficient Energy Management from a Home to Cities Exploiting Clouds Signature of Student

Abstract