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Efficient Utilization of Energy using Fog and Cloud based Integrated Environment in Smart Grid


Abstract and Figures

Smart Grid (SG) gives the opportunity of two way communication to consumers and utility. SG balances and monitors the consumption of electricity of the consumers. The concept of fog and cloud is introduced in order to enhance SG efficiency. Cloud gives computational and storage facilities to the users. The delay and latency issues arise because of its remote location. To resolve these issues, fog is introduced by cisco in recent years. The performance of cloud and fog is enhanced using a different technique. In this paper, we introduce fog and cloud based environment for the efficient utilization of energy. In this environment, Micro Grid (MG) is also attached to the buildings to make their consumption efficient using renewable resources. MG makes these buildings and societies smarter using fog and cloud based environment in SG. Fog executes those request or allocates resources to those which require less computational power and resources; otherwise, these requests are forwarded to the cloud. We apply Crow Search Algorithm (CSA) and server broker policies to calculate the Response Time (RT), Processing Time (PT) and cost.
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Efficient Utilization of Energy using Fog and Cloud
based Integrated Environment in Smart Grid
Ayesha Anjum Butt1, Nadeem Javaid1,, Amjad Rehman2and Tanzila Saba3
1COMSATS University Islamabad, Islamabad 44000, Pakistan
2MIS Department COBA, Al Yamamah University, Riyadh 11512, Saudi Arabia
3Prince Sultan University, Riyadh 11512, Saudi Arabia
Abstract—Smart Grid (SG) gives the opportunity of
two way communication to consumers and utility. SG
balances and monitors the consumption of electricity
of the consumers. The concept of fog and cloud is
introduced in order to enhance SG efficiency. Cloud
gives computational and storage facilities to the users.
The delay and latency issues arise because of its remote
location. To resolve these issues, fog is introduced by
cisco in recent years. The performance of cloud and fog
is enhanced using a different technique. In this paper,
we introduce fog and cloud based environment for
the efficient utilization of energy. In this environment,
Micro Grid (MG) is also attached to the buildings
to make their consumption efficient using renewable
resources. MG makes these buildings and societies
smarter using fog and cloud based environment in
SG. Fog executes those request or allocates resources
to those which require less computational power and
resources; otherwise, these requests are forwarded to
the cloud. We apply Crow Search Algorithm (CSA) and
server broker policies to calculate the Response Time
(RT), Processing Time (PT) and cost.
Index Terms—Fog, Cloud, Smart Grid, Smart Soci-
eties, Load Balancing, Server Broker Policies
I. Introduction
The evolution of Internet of Things (IoT) brings the
emergence of smart societies such as smart homes, smart
buildings, Smart Grid (SG), etc. The SG facilitates two
way communication between consumers and utilities to
consume and monitor the use of electricity by digital
communication. The system performs following functions:
reduce electricity consumption and cost, maximize the
consumer comfort and reliability of energy supply chain
which are done by this system [1]. The SG is introduced
to beat the limitations of the traditional grids. Smart
meters are also used by the SG to enhance its efficiency
and performance. The SG monitors and balances the
consumption of the buildings of residential, industrial and
commercial areas. Smart meter keeps track of the electric-
ity consumption of smart buildings and homes and manage
the peak hours. SG provides help to both consumers and
utility by reducing the consumption cost of consumer and
energy usage of grid in an efficient manner.
To make SG smarter, the concept of cloud computing
is introduced. By using this concept, the consumers of
different regions can get the benefits from other regions of
SG, if required. Cloud environment consists of computing
environment, which consists of numerous Virtual Machines
(VMs) and Physical Machines (PMs) inside them. Cloud
provides only computational and storage resources to
those consumers, who are utilizing the resource of it.
Furthermore, cloud provides resources to these consumers
who are particular user of it. When some other consumers
requests for resources, the delay increases due to maximum
requests. To overcome these limitations of cloud, the con-
cept of fog is introduced by "cisco" in 2014. The fog act
as an intermediary layer between cloud and consumer. It
provides ease to consumers in terms of reducing latency,
delay and provides storage also. Due to its smaller size
than cloud, different societies can purchase their own fog.
In the SG environment, the fog and cloud provide
reliability to consumers to enhance their performance. The
user can execute multiple tasks on cloud and fog VMs and
PMs. The total number of user requests process faster with
less latency using fog. In literature, different balancing
algorithms, techniques and services broker policies are
used in order to enhance the performance of cloud and
fog which reduce their computational cost [2]. With the
passage of time, the cloud and fog based environment
becomes popular in industrial societies. To make industries
smarter, cloud and fog based environment is introduced in
industries [3], [4] These environments are used to store
the big data of the industries and fog provides faster
computing services with less latency or delay.
To overcome all aforementioned challenges, we have
introduced fog and cloud integrated environment. We also
use MG with our proposed architecture. In this work, we
have considered the requests of residential societies. If the
processing time of these requests needs high computational
resources, then we transfer these request to cloud. MG
handles and monitors the consumption of industries and
homes. We use three service broker policies that are Clos-
est Data Center(CDC), Optimize RT (ORT) and Recon-
figure Dynamically with Load (RDL). The load balancing
algorithm is used to make our environment reliable and
scalable by adding fixed number of VMs and PMs inside
them. The load balancing Crow Search Algorithm (CSA) is
implemented in the scenario of residential side. To simulate
2019 International Conference on Computer and Information Sciences (2019 ICCIS) 978-1-5386-8125-1 c
2019 IEEE
the results, we have used cloud analyst simulator.
The rest of the paper is organized as follows: Section II
defines related work, Section III problem statement and
motivation. The proposed system model defines in section
IV, Section V defines the CSA Load Balancing Algorithm,
Section VI defines the simulations results and discussion.
The last Section VI defines the conclusion of this paper.
II. Related Work
One of the most valuable resources of the modern
era is energy. In previous studies, most of the research
and discussion is about smart buildings and their energy
consumption. The authors in [5] discuss the smart cities
and number of smart buildings in it. The basic goal
of their work is to minimize the energy. To fulfill their
demands and desires, [5] proposes a scheme, in which
various cloud DCs are connected to the centralized cloud
and interconnected to the grid. In this proposed scheme,
authors use renewable energy resources to minimize the
energy consumption of the grid of the smart city. For this
purpose, they use a technique named as "Novel multi-
tenant cloud based Nano grid for smart buildings". By
using this technique and proposed scheme, they achieve
scalability and performance of the system. However, with
all these respective features, they can enhance their exe-
cution time. In [6], Boroojeni et al. discuss the increasing
demand of clusters of MGs for connectivity to increase
their flexibility and security. In cluster, each MG is un-
aware about status and flow. The contribution of their
struggle is that they propose oblivious routing algorithm.
In their adopted approach, the power optimal flow problem
is solved while managing congestion and mitigating power
losses. Their proposed or adopted methodology works for
both radial and non-radial network disregarding of scale
and topology of MGs in the cluster. To evaluate the
performance and check the effectiveness of their proposed
and adopted methodology, they use Matlab simulations.
OPAL-RT real time digital simulation system. To check
the communication path between MG and cloud, they
perform OMNET++ simulations. These simulations are
run and checked practically by implementing on their
proposed system of MG and cloud environment. The main
features of their proposed system are that, system is
efficient and interoperable in terms of performance and
exchange information. However, their proposed system is
designed only for one way communication.
In Fog and cloud computing, SG becomes efficient and
intelligent with the help of emerging technology of IoT.
In [7], Cao et al. propose an effective privacy preserving
scheme for electric load monitoring which can differentiate
closer nodes in SG. In this proposed scheme, the Factorial
Hidden Markov Model (FHMM) is introduced. The main
objective of their proposed scheme is that they realize
the privacy towards fog in SG. After implementing the
scheme for achieving the key features of their proposed
technology, they propose a model of cloud, fog and SG.
To measure the performance of their scheme and check
the privacy level of their implemented model, they used
NILMTK tool on the basis of REDD data set. Subsequent
to this, their work is compared with other two schemes,
i.e., Barbosa’s and Sankar’s. They also use noise as a
parameter in their work to switch the states and check the
accuracy of their implemented scheme. The interactivity
of their proposed scheme and performance of the proposed
model is relatively better than the previous schemes and
models. On the other hand, the computational cost of their
implemented model is optimal high. The Yaghmaee et al.
in [8] propose two tier cloud based DSM for the customers
to control their residential load with less power generation
and high storage efficiency as an energy source. They
consider a power system which consists of multiple regions
and equipped with a number of MGs. According to their
research, edge cloud is utilized to find the optimal power
in each region to schedule the customer appliances. In [8],
they also propose two level optimization algorithm with a
multi level cost function. Their main aim is to minimize
the consumption cost of both consumer and utility. After
implementing the aforementioned proposed system model
and implemented algorithm, they improve the peak load
and peak to average ratio. To simulates the result of
their proposed algorithm, they use the Nash equilibrium
technique. On the other hand, they compromise the user
In [9], Djabir et al. implement a real time dynamic pric-
ing model for electric vehicles charging and discharging.
The purpose of this proposed model is to reduce the peak
load. Their implemented approach using decentralized
cloud computing architecture which is based on Software
Define Networking (SDN) and Network Function Virtu-
alization (NFV). Fog layer and SG are also part of their
proposed architecture. In their work, they aim to schedule
the requests between charging and discharging vehicles.
To fulfill their aims, they propose linear optimization
approach and real time pricing model. Stability is the main
feature of their work. On the other side, there is trade-off
between user comfort and cost.
A new identification method for smart metering is pro-
posed in [10]. This proposed method processes time and
numerical information for identification of data. To de-
identified state for statistical processing and availability
of data, they use processing of database using standardize
query language. To further check the efficiency, interop-
erability and interactivity of their system, they propose
an architecture which consists of cloud and SG. The main
objective of their proposed method and architecture is re-
identification of metering of smart data. After implemen-
tation of their proposed method, they achieve their ob-
jective, interactivity and interoperability of their system.
However, security is compromised in their system. In [11],
Rekha et al. proposes a framework of cloud computing
in SG environment by creating small integrated energy
hub which supports to handle the storage of huge data.
They want to reduce the cost, save energy and handle
large data. For these purposes, they propose stochastic
dynamic programming. In their work, they also minimize
the cost and maximize the profit for utility provider.
However, in their system, peak hours are fixed and it
also compromises the user’s comfort. In the residential,
industrial and commercial domains energy management
is required to control their power generation and con-
sumption. According to them, the energy management
helps their system to reach zero net energy in residential
domain. The IoT becomes the revolutionary change in
cost and features using actuators and sensors. However,
scalability is still issues for them. Al Faruque et al. in-
troduce a novel platform for energy management in their
work. The implemented platform introduces the feature
of interoperability and scalability. To improve the market
cost and minimize the energy consumption in their work,
they introduce fog based architecture with MGs. Their
proposed architecture consists of cloud, fog and number
of MGs that are attached with residential and commercial
buildings. When these MGs are connected to buildings,
they control and monitor their electricity consumption.
In this work, they use home energy and MG energy
management system. By using the services of IoT, they
collect the data from commercial and residential buildings.
To simulate and calculate the energy consumption of data,
they use (Million Instruction Per Second) MIPS processor,
Raspberry Pi, Zig-Bee standard, etc, are used. However, to
calculate the energy consumption of homes and buildings
they consider one home instead of multiple homes. [12].
III. Motivation and Problem Statement
In [3] and [13]- [21], proposed work discusses different
challenges and problems that are related to smart facto-
ries, cloud based architecture and other.
The industries are converted into smart industries in
[13], [14]. Cyber Physical System (CPS) interacts virtual
globe and tangible using predictive algorithm. The prob-
lems which are to be solved are equipment prognosis and
maintenance. However, there is another issue, how to solve
the problems over the internet. The range of activities
for product design is simulation, manufacturing, testing,
management and all other tasks in product life cycle [15].
However, no technique is proposed about enhancing and
assuring the production yield, which targets the product
life cycle. To solve the aforementioned challenges Ad-
vanced Manufacturing Cloud of Things (AMCOT) on the
basis of IoT, CPS and Cloud Computing is proposed in [3].
By adopting all these techniques the performance of the
industries is improved. Because of cloud platform, it han-
dles and stores large data. However, implementation cost
and delay to store large data are not discussed. To solve
the delay issue fog based integrated platform is introduced
in [16], [17]. Because of its expertise and adaptable nature,
the number of incoming requests increases. So, it becomes
difficult to schedule the tasks. To solve this issue PSO,
Energy Load Balancing (ELB), Energy Aware Dynamic
Task Scheduling (EDTS) and Critical Path Assignment
(CPA) are proposed in [18], [19], [20] and [21]. Still, cost,
performance, resource utilization are the major issues of
proposed algorithms and techniques.
This work is to devise a cloud and fog based integrated
environment using nature inspired algorithm CSA. The
aim of the proposed work is to overcome the issues of delay,
latency, system performance and minimization of cost in
terms of smart homes by utilizing the fog resources.
IV. Proposed System Model
In the proposed system model, we introduce cloud and
fog based integrated environment, which is depicted in Fig.
1. The cloud, fog, user and consumer layers are defined in
this system model.
In this proposed architecture, to overcome the limita-
tions and improve the services of cloud, fog is introduced
and integrated with the cloud. This proposed architecture
describes the flow of power in the smart city of Asia. This
architecture consists of three layers. Each layer defines
its own portrayal in the communication and power flow
process. The first layer of the architecture is known as
a cloud layer. This layer consists of the number of VMs
and PMs inside DCs. These DCs handles the requests of
the consumer that comes to the cloud from the bottom
two layers of this proposed model. This cloud provides the
communication services to fog and consumers. There is
also a two way communication between cloud and utility.
This utility act as a service provider and deals with the
requests of a cloud. There is two way communication
between cloud and utility. The cloud forwards the requests
to the consumer of SHs inside the Smart city of Asia.
The utility checks the demand of consumer according to
coming requests, then give information about the rates
of electricity that current time to the cloud. There is
also two way communication between cloud and fog. The
information about the rates of electricity forwards to the
fog from the cloud. According to the rates, the upcoming
requests to cloud again forwarded to the utility. The utility
provides a direct supply of the power to the required SH
in the particular block of the smart city. The cloud is on
the remote location of the consumer layer. The delay and
latency issue can occur during the flow of requests from
consumer and information flow from the cloud side.
To overcome the above mentioned issue, we introduce
number of fogs in our proposed system, it is because these
fogs depends on our defined number of clusters in the
smart city. The second layer is known as a fog layer.
In this proposed architecture, there are the five numbers
of fogs at the edge of the network and provides services
according to the size of the requests with low latency. It
is because this layer is closest to the consumer layer and
it also improves the delay performance of the system. In
this proposed system, fog layer have the number of PMs
and there is a number of VMs in it, these PMs are number
Block 1 Block 2 Block 5
Cluster of Buildings
Cluster of Buildings
Smart City in Asia Region
Two Way Communication
Cluster of Buildings
Power Flow
Fig. 1: Cloud and Fog Based Integrated System Model
of CPUs which are used to process the consumer and user
data. There is a two way communication between fog and
cloud and fog is located at the edge of the network. So,
it processes the requests according to its capacity and
requests are referred to a cloud. The fog provides the given
information of related energy to the consumer of the SBs
attached with it. This proposed system also provides two
way communication between fog and clusters of buildings,
between MG and fog.
MG is attached to residential consumers and fog, it
converts renewable resources into useful energy resources
and gives it to the consumer.
The third layer of this proposed architecture, which is
known as the consumer layer. In this proposed architec-
ture, the consumer is known as a residential consumer.
There is two way communication between consumer and
fog. The consumer sends a request to the fog and gets
services according to the requests. This model consists
of five numbers of clusters and each cluster has a 600-
1000 number of requests. The consumer of this proposed
architecture sends requests to the fog. The fog processes
and executes the requests the requests which have high
demands send to the cloud and other fulfilled by the MG.
The blocks which are located inside this proposed ar-
chitecture holds all the three layers of this architecture.
The utility of this proposed architecture act as a service
provider. The power is sent to the SH located inside
the smart city. The outermost boundary of the proposed
architecture shows the overall design and working of a
smart city in the Asia region. The flow of power is also
presented in this architecture that how utility supplies
power to the cluster of buildings in smart city.
V. CSA Load Balancing Algorithm
The novel meta heuristic algorithm that is based on
the nature of the crow is known as CSA. This algorithm
is proposed because the crow and corvids are considered
more intelligent birds. The algorithm works same as the
nature of the crows, i.e., crows search for food, steal and
hide the food from other crows. In this paper, CSA 1
performs load balancing. CSA initializes VMs, fogs in the
case of load balancing. Besides, the probability fitness is
calculated with respect to DC. Our environment is fog
based, so the fogs act as a source of food. Therefore, the
implemented load balancing algorithm provides help to
VMs to find the best feasible solution until they find the
best fitness value or best optimization solution value.
VI. Simulation Results and Discussion
In this paper, optimization algorithm is implemented to
utilize the energy resource efficiently. For this purpose we
implement CSA in this paper. In this section, we discuss
the RT, PT and cost on the basis of three policies. These
policies are CDC, ORT and RDL. To simulate the results
of our implemented algorithm region three is selected.
The reason to select the region three is that, this region
contains 27 million consumers. This region is known as
Asia. It has been further subdivides in several regions
that are central, South, East, Southern Asia, etc. These
continents contains large number of commercial, industrial
and residential smart buildings. The total population of
Algorithm 1: CSA
2Randomly initialize the position of a flock of C
crows in the search space
3Search for list of VM and Fog
4j=F og
5i=V M
6for t = 1:24 do
7Let Yis a random position of search space
8Evaluate the position of the CVM
9Initialize the memory of each CVM
10 while iter itermax do
11 Determine the VM_Size
12 Compute the processing time using equation
13 Calculate Transmission delay using equation
14 Compute the RT using equation 3
15 for i=1:Cdo
16 Randomly get a CVM jto follow i
17 Define awareness probability
18 if rjAP j,iter then
19 xi,iter1=xi,iter +riX(mj,iter xi,iter)
20 else
21 xi,iter+1 =Y
22 end
23 end
24 end
25 Check the feasibility of new fog
26 Evaluate the new position of the CVM
27 end
28 End
this region is 43,810,582 who lives in the smart societies
of this region. To make the efficient utilization of resources
and optimize energy different algorithms are used.
In this paper, CSA is used for this purpose. On the
basis of this paper scenario, result is simulated on the basis
of five clusters and these cluster contains 600-1000 users.
After simulation on the basis of aforementioned policies,
it is proven that CSA has better RT and PT on the basis
of CDC and ORT policies. RDL has maximum RT and
PT in Fig. 2. The total cost of CDC and ORT is also
minimum as compared to RDL in Fig. 3. The RDL is not
fully implemented in cloud analyst scenario that’s why it
takes maximum RT, PT. Increase in cost has also same
issue as mention above.
The formula which is used to calculate the processing
time is given in below:
P T =TR /V Mspeed (1)
. Where TR is the total requests of the consumers and
V Mspeed is the speed of VMs. The transmission delay is
computed as given below:
T D =T otall+T otalT D (2)
Where lis latency and TD is the transmission delay.
The formula which is used to calculate the response time
RT =FTAT+T D (3)
. Where F Tis the finish time, A Tis the arrival time and
TD is the transmission delay.
A. Response Time
In this section, the RT is calculated on the basis of
clusters. These clusters contain 600-1000 number of SHs.
The RT is calculated on the basis of number and size of
The Table I shows the avg. RT of clusters on the basis
of aforementioned three service broker policies. The RT of
CDC is 6.58%, RT of ORT is 7.72% and RT of RDL is
TABLE I: Avg. RT of clusters on the basis of Policies
User Base CDC (ms) ORT (ms) RDL (ms)
C1 79.60 93.59 1062.27
C2 79.63 93.63 1055.84
C3 79.78 94.24 1036.51
C4 80.01 93.58 1024.06
C5 79.95 93.50 1009.83
Service Broker Policies
Avg. RT and PT (ms)
Avg. RT (ms)
Avg. PT (ms)
Fig. 2: Average RT and PT
TABLE II: Avg. PT of fogs on the basis of Policies
Fogs CDC (ms) ORT (ms) RDL (ms)
Fog1 24.47 39.99 425.89
Fog2 31.53 45.07 1126.54
Fog3 31.89 45.38 1121.32
Fog4 31.64 45.43 1126.57
Fog5 31.39 45.39 1126.27
B. Processing Time
In this section, the PT of the DCs is defined. The PT of
DCs depends on the number and size of the components.
The PT of DCs of all three policies; CDC, ORT and RDL
is shown in Table II. The PT of CDC is 2.84%, PT of ORT
is 4.17% and RDL is 92.97%. The RDL takes maximum
processing time as compared to other two policies because
ORT is not full implemented in cloud analyst tool.
C. Cost
In this section, the cost is defined. This total cost
depends on the number of resources and data transfer cost,
etc. In Fig. 3, it is defined that the total cost of policy
CDC and ORT is less than the cost of RDL. The VM
cost, Transfer cost and MG cost are also compared on the
basis of three policies in Fig. 3.
Service Broker Policies
Cost ($)
VM Cost
Transfer Cost
MG Cost
Total Cost
Fig. 3: Total Cost
VII. Conclusion
The industrial and commercial societies are becoming
smarter day by day. In this paper, a fog and cloud based
architecture is proposed. This architecture is used to make
an efficient utilization of energy consumption in SG. MG
also plays an efficient role in SG. We consider average RT,
PT and total cost of this proposed architecture. The CSA
is used to balance the load of a network along with three
service broker policies: CDC, ORT and RDL. The RT with
CDC is 66.04% and ORT is 77.53%. The PT with CDC is
2.84% and ORT is 4.15%. On the other hand RT and PT,
with CSA and RDL is not good. The average RT, PT and
total cost of RDL is 92%, 57.79% and 57.79%. In future,
we will consider VM migration problem.
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... Cloud and fog are introduced to enhance smart grid efficiency. Fog and cloud-based environment has been considered for efficient energy utilization [25]. Fog allocates resources closer to the sources of requests which require less computational power and resources, otherwise, those requests will be outsourced to the remote cloud. ...
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Smart Grid benefits from information and communications technology (ICT) to integrate data from different sources across the network. The growing market for electric vehicles (EV) and electric devices is increasing energy demand. To manage a large amount of complex intelligent equipment, EV charging and discharging, and data-intensive devices, a reliable modern smart grid with a service-oriented, secure, efficient, and cost-effective system is compelling. Cloud computing is a promising approach to achieve that objective as monitoring power grid data flow and data center management are the key. This paper proposes an optimization model for energy management within the power cloud. Our energy system model enables accessible services to computing resources and real-time data stream processing within an integrated environment. Using digital technology, the proper implementation of our model reduces costs and energy consumption and improves the smart grid reliability for customers and energy providers in a distributed manner. This paper presents the implementation of a three-procedure optimization algorithm within a novel Multi-Agent Cloud-fog Structure (MACS) to meet the requirements raised by smart grid communication and distribution. Our model promotes reliable energy consumption adjustments by end-users who can choose power supplied by solar, wind, geothermal, biomass, or other renewable sources or from non-renewable sources in ways that reveal opportunities for demand-supply balance and energy saving.
In this paper, a new framework based on the directed acyclic graph (DAG) and distributed multi-layer cloud-fog computing to find the optimal energy management of the smart grids, considering high penetration of plug-in hybrid electric vehicles (PHEVs). The presented distributed structure lets neighboring agents make a consensus together. The uncertainties have been modeled according to the Monte Carlo simulations, due to wide usages of diverse renewable energy resources such as photovoltaic panels and wind turbines. Three diverse charging schemes have been considered in the smart grid test system which contains controlled, uncontrolled and smart chargings. The Whale Optimization Algorithm (WOA) has been used to solve the augmented Lagrangian function in each agent. The simulation results are shown that the suggested scheme is effective.
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A smart grid (SG) is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services. Moreover, it plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility. Resource management is one of the big issues in SGs. In this paper, we propose a cloud–fog–based model for resource management in SGs. The key idea of the proposed work is to determine a hierarchical structure of cloud–fog computing to provide different types of computing services for SG resource management. Regarding the performance enhancement of cloud computing, different load balancing techniques are used. For load balancing between an SG user’s requests and service providers, five algorithms are implemented: round robin, throttled, artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization. Moreover, we propose a hybrid approach of ACO and ABC known as hybrid artificial bee ant colony optimization (HABACO). Simulation results show that our proposed technique HABACO outperformed the other techniques.
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Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non-intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.
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Various security threats exist in the smart grid environment due to the fact that information and communication technology are grafted onto an existing power grid. In particular, smart metering data exposes a variety of information such as users’ life patterns and devices in use, and thereby serious infringement on personal information may occur. Therefore, we are in a situation where a de-identification algorithm suitable for metering data is required. Hence, this paper proposes a new de-identification method for metering data. The proposed method processes time information and numerical information as de-identification data, respectively, so that pattern information cannot be analyzed by the data. In addition, such a method has an advantage that a query such as a direct range search and aggregation processing in a database can be performed even in a de-identified state for statistical processing and availability.
Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. Firstly, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization (PSO) algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built towards the manufacturing cluster. Finally, a multi-agent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.
The introduction of Industry 4.0 and rapid development of Manufacturing Cyber-Physical Systems (MCPS), as well as the increasing demand for multi-variety, small batch and personalized customization, pose a huge challenge to the traditional manufacturing systems. In order to meet the production requirements for fast iteration and realize agile and efficient manufacturing resource allocation, this paper proposes an ontology-based resource reconfiguration method from the perspective of resource utilization. First, an intelligent device ontology that describes the intelligent manufacturing resource is established using the Web Ontology Language (OWL). On this basis, the relational database is associated with the ontology of manufacturing system, which makes the manufacturing resources be mapped to the model instances. Finally, we analyze the equipment reconfiguration of intelligent manipulator as an application case, which explains the proposed method for resource reconfiguration based on ontology, and verifies its feasibility in manufacturing. Lastly, this study provides a new method for reconfigurable research of manufacturing resources.
Smart grids (SG) energy management system and Electric Vehicle (EV) has gained considerable reputation in recent years. This has been enabled by the high growth of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand and to manage building energy during the day, it is important to perform an intelligent scheduling for EVs charging and discharging service and buildings areas by including different metrics, such as real time price and demandsupply curve. In this paper, we propose a real-time dynamic pricing model for EVs charging and discharging service and building energy management, in order to reduce the peak loads. Our proposed approach uses a decentralized cloud computing architecture based on Software Define Networking (SDN) technology and Network Function Virtualization (NFV). We aim to schedule user’s requests in a real-time way and to supervise communications between micro-grids controllers, smart grid and user entities (i.e., EVs, Electric Vehicles Public Supply Stations (EVPSS), Advance Metering Infrastructure (AMI), smart meters, etc.). We formulate the problem as a linear optimization problem for EV and a global optimization problem for all micro grids. We solve the problems using different decentralized decision algorithms. To the best of our knowledge, this is the first paper that proposes a pricing model based on decentralized cloud-SDN architecture in order to solve all the aforementioned issues. The extensive simulations and comparisons with related works proved that our proposed pricing model optimizes the energy load during peak hours, maximises EVs utility and maintains the micro grid stability. The simulation is based on real electric load of the city of Toronto.
As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufacturing industries. Aimed at addressing the abovementioned two issues, in this paper, a five-stage approach for enhancing and assuring yield is proposed. Also, a smart manufacturing platform-AMCoT (Advanced Manufacturing Cloud of Things) based on IoT (Internet of Things), CC (cloud computing), BDA (big data analytics), CPS (cyber-physical systems), and prediction technologies is designed and implemented to realize the proposed five-stage approach of yield enhancement and assurance. Finally, AMCoT is applied to a bumping process of a semiconductor company in Taiwan to conduct industrial case studies. Testing results demonstrate that AMCoT possesses capabilities of conducting total inspection in production, providing prognosis and predictive maintenance on equipment, finding the root cause of yield loss, and storing and handling big production data, which as a whole is promising to achieve the goal of zero defects.
Due to the increasing sizes of cloud data centers, the number of virtual machines (VMs) and applications rises quickly. The rapid growth of large scale Internet services results in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm (LVMM). At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we balance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of network resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMMalgorithm can effectively balance the load of network resource in cloud computing. © 2017 Higher Education Press and Springer-Verlag Berlin Heidelberg
Fog Computing is an emerging paradigm, suitable to serve the particular needs of IoT networks. It includes the deployment of computational devices at the edge of the network facilitating faster real-time processing of time-sensitive data. In this article, we present a Fog architecture, which diverges from the traditional hierarchical and centralized Fog model, and adopts a cooperative model, which allows for a federation of Edge networks. In our proposal, the tasks that the nodes are called to complete, are characterized according to their computational nature and are subsequently allocated to the appropriate host. Edge networks communicate through a brokering system with IoT systems in an asynchronous way via the Pub/Sub messaging pattern.
Energy is one of the most valuable resources of the modern era and needs to be consumed in an optimized manner by an intelligent usage of various smart devices, which are major sources of energy consumption nowadays. With the popularity of low-voltage DC appliances such as-LEDs, computers, and laptops, there arises a need to design new solutions for self-sustainable smart energy buildings containing these appliances. These smart buildings constitute the next generation smart cities. Keeping focus on these points, this article proposes a cloud-assisted DC nanogrid for self-sustainable smart buildings in next generation smart cities. As there may be a large number of such smart buildings in different smart cities in the near future, a huge amount of data with respect to demand and generation of electricity is expected to be generated from all such buildings. This data would be of heterogeneous types as it would be generated from different types of appliances in these smart buildings. To handle this situation, we have used a cloudbased infrastructure to make intelligent decisions with respect to the energy usage of various appliances. This results in an uninterrupted DC power supply to all low-voltage DC appliances with minimal dependence on the grid. Hence, the extra burden on the main grid in peak hours is reduced as buildings in smart cities would be self-sustainable with respect to their energy demands. In the proposed solution, a collection of smart buildings in a smart city is taken for experimental study controlled by different data centers managed by different utilities. These data centers are used to generate regular alerts on the excessive usage of energy from the end users' appliances. All such data centers across different smart cities are connected to the cloud-based infrastructure, which is the overall manager for making all the decisions about energy automation in smart cities. The efficacy of the proposed scheme is evaluated with respect to various performance evaluation metrics such as satisfaction ratio, delay incurred, overhead generated, and demand-supply gap. With respect to these metrics, the performance of the proposed scheme is found to be good for implementation in a realworld scenario.