Access to this full-text is provided by Tech Science Press.
Content available from Computers, Materials & Continua
This content is subject to copyright. Terms and conditions apply.
Copyright © 2024 The Authors. Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ech
T
PressScience
DOI: 10.32604/cmc.2024.056941
ARTICLE
Design and Develop Function for Research Based Application of Intelligent
Internet-of-Vehicles Model Based on Fog Computing
Abduladheem Fadhil Khudhur*, Ayça Kurnaz Türkben and Sefer Kurnaz
Department of Electrical and Computer Engineering, Altinba¸s University, Istanbul, 34000, Turkey
*Corresponding Author: Abduladheem Fadhil Khudhur. Email: 203720061@ogr.altinbas.edu.tr
Received: 03 August 2024 Accepted: 08 October 2024 Published: 19 December 2024
ABSTRACT
e fast growth in Internet-of-Vehicles (IoV) applications is rendering energy eciency management of vehicular
networks a highly important challenge. Most of the existing models are failing to handle the demand for energy
conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing,
this paper proposes a new model to overcome energy-inecient vehicular networks by simulating large-scale
network scenarios. e main inspiration for this work is the ever-growing demand for energy eciency in IoV-
most particularly with the volume of generated data and connected devices. e proposed LEAF model enables
researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous
infrastructures. Among the possible reasons is that it provides a realistic simulation environment in which compute
nodes can dynamically join and leave, while dierent kinds of networking protocols-wired and wireless-can also
be employed. e novelty of this work is threefold: for the rst time, the LEAF model integrates online decision-
making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with
eciency optimization in mind. Unlike existing fog computing simulators, data ows and power consumption are
modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a
wide range of devices and applications. e results of evaluation show that LEAF can cover up to 98.75% of the
distance, with devices ranging between 1 and 1000, showing signicant energy-saving potential through A wide-
area network (WAN) usage reduction. ese ndings indicate great promise for fog computing in the future-in
particular, models like LEAF for planning energy-ecient IoV infrastructures.
KEYWORDS
Fog computing; internet of vehicles; LEAF; segmentation; distance; power consumption; cloud; vehicle nodes;
wireless
1Introduction
Fog computing has become evident that the performance per watt drops dramatically when the
target is under low load for vehicular networks. This inefficiency is a huge problem: idle servers can
require up to 60% of their peak power draw as mentioned in [1], on average, cloud data centers are
only utilized at 20% to 30% of their full capacity. Energy-proportional data centers and networks
are popular because they can operate efficiently in underutilized situations. In practice, there are
3806 CMC, 2024, vol.81, no.3
various open technical challenges in reducing the static energy consumption of hardware as mentioned
in [2]. While Central Processing Units (CPUs) became more and more energy-proportional over
the years thanks to energy-saving mechanisms like dynamic voltage and frequency scaling (DVFS),
other components like memory, network, and networks are traditionally energy disproportional for
vehicular networks as mentioned in [3]. Static energy consumption of resources like network adapters
or routers can make up to more than 90% of their maximum energy consumption as mentioned in
[4]. Nevertheless, due to many technological advances over the last decade, modern hyperscale data
centers can operate in extremely energy efficient manners for vehicular networks as mentioned in [5].
This means a huge potential for energy savings lies in the reduction of static energy consumption.
Less attention has been devoted to the question of what extent fog computing may affect the overall
power usage of network infrastructure. On the one hand, it has been shown that fog computing can
highly reduce network traffic due to its geo-proximity to data producers, which, as a consequence,
also lowers the network’s power usage. Furthermore, Nano data centers usually require less cooling
than bigger data centers as mentioned in [6]. On the other hand, hyperscale data centers are highly
optimized for energy-efficiency and power proportionality, and some recent studies suggest that their
overall consumption may be significantly lower than previously assumed for vehicular networks as
mentioned in [7]. Large-scale deployments of distributed fog infrastructure will inevitably impose
additional power requirements and it is questionable whether a performance per watt comparable
to that of centralized data centers can be achieved. Since large-scale, real-life deployments of fog
computing infrastructure are still to come, research on new communication protocols and scheduling
algorithms is mostly limited to theoretical models and emulation tools. Over the last four years,
numerous emulators, simulators, and analytical models for vehicular networks as mentioned in [8]
have been proposed. Since version 2.0, fog computing features an energy model proposed by [9]that
allows modeling the power usage of CPUs. Apart from being relatively simplistic, its main limitation is
that it cannot be used in conjunction with the vehicular networks model. The fog computing extension
addresses this by introducing energy-aware network components that even allow modeling the energy
consumed by VM (Virtual Machine) migrations. However, it introduces a lot of conceptual and
computational complexity, is coupled towards modeling inter-data center traffic, and does not support
different link types like wireless connections. Because of that, this energy model is not appropriate
for modeling energy consumption in fog computing environments. Recently, IoV has also become a
hot topic with the increasing number of connected vehicles. In general, how to handle the increasing
demand for computing resources and power efficiency are considered the most important factors. IoV
networks contain millions of interacted devices that produce, process, and disseminate data at real-time
speed. Large-scale networks are prone to many problems, and their huge power consumption and
performance optimization will be a great challenge. Furthermore, traditional cloud-based solutions
will fall short in addressing latency-sensitive and energy-intensive IoV demands; this compels the need
to investigate other approaches, such as fog computing.
It further elaborates that with the scaling of computing on the need for efficiency, together
with the unique demands imposed by IoV networks, this research is warranted. This is because the
still-increasing size and complexity of vehicular networks continuously dramatize cloud computing
deficiencies related to latency and energy efficiency. Fog computing might be one of the possible
avenues; however, existing simulators and frameworks do not consider large-scale scenario simulations
or have their focus on energy consumption at a fine-grained level. The big challenge is to design a
fog computing model that can support large-scale vehicular networks with energy-aware mechanisms
for task placement and routing strategy optimizations. Besides this, it should consider the dynamic
scenario of devices joining and leaving the network, a common reality for vehicle mobility. Basically,
CMC, 2024, vol.81, no.3 3807
addressing these challenges provides a clear insight into IoV research and future infrastructure
planning.
1.1 Problem Statement
Existing simulators cannot sufficiently model energy consumption of fog computing environments
in large-scale experiments. There is a need for a simulator that allows identifying the causes of
power usage and enables research on energy-efficient architectures and algorithms to fully exploit
the fog’s potential for conserving energy. Even with research into fog and cloud computing, the
gap still remains in managing large-scale dynamic vehicular networks with considerations for energy
conservation. No existing model and simulator are capable of real-time simulation of large-scale IoV
environments where frequent joinings and leavings occur, nor do they optimize energy consumption
across heterogeneous infrastructures. Conclusively, this leads to increased energy consumption by
Wide-Area Networks (WANs) in such systems; thus, current solutions are not adequate for any
sustainable future deployment. In this respect, there is a pressing need for a scalable and energy-aware
fog computing model that could address these deficiencies while maintaining the high performance
demanded by IoV applications. Derived from these use cases and considering the numerous challenges
in modeling fog computing environments, the following requirements are placed on the proposed
model:
(1) Internet-of-Vehicles Based Realistic Fog Computing Environment: The model enables the
simulation of distributed, heterogeneous, and resource-constrained fog computing environments,
where compute nodes are linked with different types of network connections. The model ensures
location-awareness of nodes, for example, to calculate close by mobile access points. Nodes can be
mobile; namely, their location can change according to predetermined or randomized patterns, and
they can join or leave the topology at any time. The model enables the simulation of different streaming
applications, from linear data pipelines to applications with complex topologies, including a multitude
of data sources, processing steps, and data sinks.
(2) Internet-of-Vehicles Based Holistic Energy Consumption Model: The model is able to assess the
power usage of all data centers, edge devices, and network links in the fog computing environment
at any point in time during the simulation. Hence, it allows for the creation of detailed power
usage profiles for individual parts of the infrastructure. Additionally, the model makes the energy
consumption of infrastructure traceable to the responsible applications to let researchers identify
inefficiencies and opportunities to improve in the network’s architecture.
(3) Internet-of-Vehicles Based Energy-Aware Online Decision Making: The model enables simu-
lated schedulers, resource management algorithms, or routing policies to assess the power usage of
individual compute nodes, network links, and applications during the run time of the simulation
and, hence, allows for energy-aware decisions. This enables research on algorithms such as energy-
efficient task placement strategies or routing policies. Furthermore, the model enables said algorithms
to dynamically apply energy-saving mechanisms such as powering off idle fog nodes.
(4) Fog Computing Performance and Scalability: The model allows the simulation of complex
scenarios with hundreds of parallel existing devices that execute thousands of tasks. The runtime of
simulations of such scale is at least two orders of magnitude faster than real-time.
3808 CMC, 2024, vol.81, no.3
1.2 Aim of Study
The contribution to be made by this research work is called the LEAF computing model, a new
framework developed for the simulation of a large-scale vehicular network with prime consideration
of energy efficiency. The main contribution of this paper focuses on the following principal aspects:
•Development of the LEAF Model: A scalable and energy-aware fog computing model that will
be able to support thousands of streaming applications and dynamically changing vehicular
networks.
•Energy-Aware Algorithms: The energy-aware task placement and routing algorithms will be
integrated together using real-time power usage data for performance optimization.
•Hybrid Empirical Results: Model the data flows and power consumptions with a hybrid
approach by combining analytical and numerical methods, hence enabling scalability while
reducing computational overhead.
•Evaluation and Results: Extensive simulations show that LEAF can save up to 98.75% of the
distance for devices between 1 and 1000, reducing WAN usage significantly and allowing
promising energy savings.
2Background
As more and more devices become connected to the internet, the amount of produced data is
soon expected to overwhelm today’s network and compute infrastructure in terms of capacity as
mentioned in [10]. In their latest annual internet report, Cisco states that there are already 8.9 billion
machine-to-machine connections in 2025, and they expect this number to grow to 14.7 billion by
2023 as mentioned in [11]. Furthermore, there are serious privacy and security concerns associated
with overcentralizing the processing and storage of IoT (Internet of Things) data as mentioned in
[12]. Energy management in vehicular networks has been studied in several works. The author in [13]
proposed a cloud-based energy-efficient framework for IoT (Internet of Things), with one of the key
concepts being the offloading of tasks from end devices to cloud servers. Along this line, Reference [14]
proposed a hybrid cloud-fog architecture aiming to minimize latency in IoT environments. However, all
these solutions rely on WANs, whose inefficiencies can arise in the form of higher energy consumption
and slower response times when the number of deployments increases.
Recent advances in fog computing, extending cloud computing capabilities to the edge of the
network, provide a promising alternative. Due to the proximity to the end devices in IoV networks, fog
computing can typically attain lower latency and power consumption. However, none of the existing
fog-based models is sufficiently capable of simulating the dynamic, large-scale, and heterogeneous
nature of vehicular networks. Various limitations must be overcome by developing innovative solutions
that include energy-aware algorithms and scalability for thousands of devices.
Fog and edge computing describe computing paradigms targeted at solving the problems posed
by the rise of IoT. A distributed layer of smaller data centers, termed Nano data centers, cloudlets,
or fog nodes, is supposed to process data close to its producers-the so-called edge of the network as
mentioned in [11]. Edge devices can be anything from mobile phones or air quality sensors to connected
cars and traffic lights. The geo-proximity to end-users can lead to highly reduced latencies for real-time
applications, as well as less overall network traffic. Fog nodes can either directly respond to actuators
or at least preprocess, filter, and aggregate data to reduce the volume sent over the WAN as mentioned
in [15]. Moreover, many applications do not require centralized control and could instead operate-
and therefore, scale in a fully distributed manner. An example is smart traffic management, where
CMC, 2024, vol.81, no.3 3809
connected cars and traffic lights can act by communicating purely among themselves as mentioned in
[16]. Nevertheless, fog computing is mostly meant to complement cloud computing, not replace it.
From a privacy point of view, fog nodes can anonymize data before forwarding it to prevent misuse
by the cloud provider or another third party. For example, more and more smart meters, namely
electricity meters with internet access, are being deployed. They regularly report the current power
consumption to network operators to enable more efficient control of the electricity network. However,
the raw sensor data might contain confidential information, such as when users switch the television
on or off. Fog nodes could, for instance, aggregate and anonymize the measured data from different
households to ensure privacy as mentioned in [16].
Besides fog computing, there exist numerous related computing paradigms: Edge computing,
mist computing, mobile cloud computing, and cloudlet computing, to name a few. Despite attempts
to highlight differences in these approaches, like the proximity of computation to the edge of the
network, or the adoption of pure peer-to-peer concepts, they mostly describe similar ideas. Further, the
terminology is sometimes used inconsistently across research papers, which can be attributed to the fact
that the research field is still very young as mentioned in [17]. In recent years, a consensus has emerged
that edge computing refers to computing on-or extremely close to-the end devices, while the fog is
located between edge and cloud. For the course of this thesis, fog computing refers to all fog and edge-
related computing paradigms. LEAF is f lexible enough to model all kinds of distributed topologies,
including pure peer-to-peer architectures. Although fog computing is a rapidly growing field, as of
today, there exist almost no commercial deployments on which researchers can conduct experiments.
Moreover, setting up, managing, and maintaining a large number of heterogeneous devices incurs high
operational costs. To facilitate research on fog computing, a variety of emulators and models have been
presented in recent years. This section aims to provide some background on simulation and modeling
as mentioned in [18].
Emulators enable the execution of real applications in virtual fog computing environments. By
introducing latencies or failure rates among virtualized compute nodes, the emulator mimics specific
characteristics of the fog environment during execution. This approach approximates reality closely
and allows for empirical results but has one major drawback: The execution of applications in emulated
environments is computationally even more expensive than running them natively as mentioned in
[19]. This constraint can be mitigated up to a certain point by the scalability of cloud computing.
Nevertheless, large-scale experiments with thousands of devices and applications soon become very
costly.
A different approach towards research on fog computing environments is the development and
usage of models. A model abstracts some of the fog environment’s underlying details and thus can
make experiments more efficient, scalable, and most importantly-easier to analyze. Creating and
applying models can result in a more comprehensive understanding of the problem space by identifying
“driving” variables as mentioned in [20]. Yet, modeling is in-variably a trade-off between realism and
simplicity, and what makes a good model always depends on the use case.
The execution of models is called simulation. A considerable advantage of simulation to other
approaches is that its runtime is not necessarily bound to real-time. A sufficiently abstract simulation
could, for instance, reproduce several hours of a fog computing environment’s behavior in a few
minutes. However, modeling also has its pitfalls: Wrong or biased assumptions can lead to invalid
simulation results. This is especially relevant in fog computing environments where almost no real-
life deployments of infrastructure could be used for validation by comparison. Numerical network
models are almost invariably so-called Discrete-Event Simulations (DES) in fact all simulators. A
3810 CMC, 2024, vol.81, no.3
DES is constituted of a queue of events that each take place at a certain time. Events get processed in
chronological order, and each processing step may result in new events that are placed in the queue
as mentioned in [21]. It is assumed that the system state between consecutive events does not change;
consequently, the simulator can jump directly from event to event. This allows simulations faster than
in real-time. The simulation is over if there are no more events in the queue, or a predefined time limit
has been reached. A DES scales roughly linearly with the number of events, but due to its sequential
nature, it is particularly hard to parallelize as mentioned in [22]. A general measure for describing the
energy effectiveness of hardware is performance per watt, namely the rate of computation that a piece
of hardware can provide for every watt of consumed power. Depending on the field of application,
the metric used to specify performance varies: Scientific computing and computer graphics require
large amounts of floating-point calculations, which is why the performance of supercomputers and
GPUs (Graphic Processing Unit) is described in FLOPS (Floating-Point Operations Per Second).
The performance of non-scientific and general-purpose computing units is mostly expressed in MIPS
(Millions of instructions per second) or other comparable benchmarks. As IoT applications are very
diverse, in the following MIPS are used to determine a compute node’s performance as mentioned
in [23]. When designing detailed models of computing infrastructure, it is possible to determine
performance per watt metrics for additional components like memory, hard discs, mainboards, fans,
or the power supply itself. Nevertheless, there is usually a clear correlation between MIPS and
memory and storage access. Thus, in higher-level models, it is reasonable to describe a compute node’s
performance per watt based only on its computational load.
In [24], it came up with a hybrid cloud-fog computing framework for IoV networks and presented a
task offloading model to achieve optimality with minimum latency. Therefore, their model offers better
response times compared to the cloud-only model. Although this approach resulted in significant
reductions in the delays of executing the tasks, it did not concentrate on how energy consumption
could be optimized-a major concern in large-scale IoV deployment where the number of devices is
continuously increasing.
In [25], it proposed an architecture for vehicular networks based on edge computing. They
underlined how there is a real need for the computation to be fully localized since complete reliance
on cloud servers hosted at a centralized location can result in much overhead. Although their work
demonstrated how edge computing can reduce latency, their focus remained on task offloading and
did not adequately explore energy-saving mechanisms. This calls for more energy-aware solutions,
especially when scaling up to thousands of devices.
In [26], it proposed an energy-aware fog computing model exclusively for IoT applications. Energy-
efficient task scheduling and resource management algorithms were introduced in their model that
reduced the overall power consumption by 15%; however, their system was limited to small and
medium-sized IoT deployment and hence could not be extended to large-scale scenarios characterizing
IoV environments.
In [27], it presented a novel fog computing architecture for VANET (Vehicular ad-hoc network).
In this architecture, the hierarchical structure was utilized by the authors to handle the fog nodes
over a wide area and investigate the potential for latency reduction with a great magnitude. However,
improving the efficiency in communication was the focus of that work, and energy-aware mechanisms
were not considered. This scalable framework, while addressing one major issue, left the energy
optimization problem nearly unaddressed.
In [28], it proposed an efficient fog computing model in large-scale IoV networks. In the year 2024,
energy efficiency was attained through the implementation of optimized scheduling of tasks. Energy
CMC, 2024, vol.81, no.3 3811
consumption was predicted by machine learning algorithms and allocated resources dynamically to
those fog nodes which consumed the least power. Though this method showed a reduction of energy
consumption by about 20%, this system relied on WAN connections. Besides, this is less energy-
efficient when the usage rate of WAN is high as shown in Tabl e 1 .
Table 1: Comparison of the proposed LEAF model with existing schemes by [29] based on key
performance metrics
Criteria Proposed scheme
(LEAF)
[24][28]
Energy consumption (kWh) 150 200 180
Latency (ms) 20 35 30
Scalability (No. of devices) High (1000+
devices)
Medium (500
devices)
Medium (700 devices)
Task placement efficiency 95% 85% 88%
WAN usage Low High Medium
Power savings (%) 25% 10% 15%
Simulation time (s) 100 150 120
In [29], it presented an energy-efficient fog computing model for vehicular networks. The proposed
model was based on the fact that a proper power usage analysis was considered in detail. It permitted
dynamic vehicular mobility, emulating practical conditions in which the join and leave of vehicles
within a network happened quite frequently. The derived solution was computationally expensive and
thus not scalable for bigger networks. This deploys the challenge of scalability, energy efficiency, and
real-time performance, which is still key in IoV systems.
3Methodology
This research introduces the Large Energy-Aware Fog (LEAF), a simulator that aims to eliminate
the short-comings of existing models, thus enabling comprehensive modeling of Large Energy-Aware
Fog computing environments. The most evident deficiency of existing fog computing simulators is
that, if at all, only the energy consumption of data centers and end devices is simulated. However, for
obtaining a holistic energy consumption model, the network must be considered too. The simulators
presented feature network modeling, and did this very thoroughly. They model every single network
package individually, sometimes even implementing entire communication protocols like TCP (Trans-
mission Control Protocol). The Vehicular OBU Capability (VOCC) dataset was used in this research
using the LEAF technique for evaluation. This degree of detail negatively impacts performance.
Demands scalability, LEAF takes a different approach: Network links are considered resources,
similar to compute nodes, that have certain properties and constraints and a particular performance
per watt. Data flows between tasks and allocates bandwidth on these links. The dataset was acquired
from KAGGLE which is https://www.kaggle.com/datasets/haris584/vehicular-obu-capability-dataset,
accessed on 20 January 2023. Modeling networks in this more abstract fashion is inspired by analytical
approaches. It enhances the performance of the DES because fewer events are created. Furthermore,
the analysis of a model’s state at a particular time step becomes very easy: The user can comprehend
the current network topology, all data flows, and allocated resources of running applications solely
3812 CMC, 2024, vol.81, no.3
by observing the simulation state at any point in time. A disadvantage of this kind of modeling is the
higher level of abstraction, and therefore, possibly less accurate results.
Fig. 1 depicts an exemplary infrastructure graph with a multitude of different compute nodes and
network links. In the bottom left, several sensors form a Bluetooth Low Energy (BLE) mesh network,
which connects to a fog computing layer. The fog nodes in this layer are interconnected via the passive
optical network (PON) and can communicate with cloud nodes via WAN. The bottom right shows
several smart meters that connect to public fog infrastructure via Ethernet. Furthermore, connected
cars are linked to these fog nodes, as well as to each other for Vehicle-To-Vehicle (V2V) communication
via Wi-Fi. The public fog nodes connect the cloud via WAN with a 4G LTE (Long term Evolution)
access network as shown in Tabl e 2 .
Figure 1: Complex infrastructure graph and flows with various compute nodes and network links
CMC, 2024, vol.81, no.3 3813
Table 2: Simulation parameters for the proposed LEAF model, detailing the MAC protocol, data
propagation model, vehicle density, and tools used for network and traffic simulation
Simulation parameter Value/Description
MAC protocol IEEE 802.11p
Data propagation model Two-ray ground model
Simulation duration 1000 s
Vehicle density 50–200 vehicles per km2
Number of vehicles 500
Radio communication range 300 m
Customized network simulator LEAF simulator (Based on fog computing)
Traffic simulator SUMO (Simulation of Urban Mobility)
In the proposed model, the simulation environment was configured in such a way that the realistic
conditions of vehicular networks were replicated properly. The details of the simulation parameters
that have been used to evaluate the LEAF model are furnished in Tabl e 2 . Here, the authors preferred
to use the IEEE 802.11p MAC (Multiple Access Control) protocol since it has already become quite
effective and reliable in many VANET communications where vehicles are moving at high speeds. The
Two-Ray Ground model was selected to simulate the propagation of data, which gives a good model
for signal transmission in open areas, a frequent case in vehicular environments.
This was simulated for 1000 s to give ample time to observe performance metrics under variable
conditions. The vehicle density ranges from 50 to 200 vehicles per square kilometer, while the number
of simulated vehicles is 500. The radio communication range was set to 300 m in order to ensure
realistic communications between the vehicles. By leveraging the principles of fog computing, the
performance analysis of energy consumption and efficiency of task placement has been carried out
using a customized LEAF simulator. Meanwhile, the SUMO (Simulation of Urban Mobility) traffic
simulator was integrated, enabling realistic vehicle movements and traffic conditions to be simulated-
thus, providing a complete extensible evaluation framework for the proposed model.
Lastly, in the top right, two devices are directly connected to the cloud via WAN using a Wi-Fi and
5G access network. Note that the access network, for example, wireless routers or mobile base stations,
is usually not explicitly modeled in LEAF. Instead, the user should determine the overall bandwidth
and power usage of the entire WAN link. Two vehicles are on the map, connected to nearby traffic
lights, and two traffic lights have fog nodes attached. Note that this is just an example; the number of
vehicles and fog nodes varies in the experiments as shown in Fig. 2.
Although some components of LEAF are based on analytical approaches, at its core it remains
a numerical model to be exact a DES (Data Encryption Standard). The user can adjust the infras-
tructure’s topology, the placement of applications, and the parameterization of resources over time.
This flexibility has two decisive advantages over purely analytical models: First of all, fog computing
environments can be represented in a much more realistic way. LEAF allows for dynamically changing
network topologies, mobility of end devices, and varying workloads. Secondly, LEAF enables online
decision making during the simulation, enabling research on energy-aware task placement strategies,
scheduling algorithms, or traffic routing policies.
3814 CMC, 2024, vol.81, no.3
Figure 2: Map of the simulated city center with two vehicles and two fog nodes
The source task is running on a sensor and sends 1 Mbit/s to the processing task. The processing
task requires 3000 MIPS and emits 200 kbit/s to the sink task, which, for example, stores the results
in cloud storage as given in Fig. 3. A task placement strategy can now decide on a placement of the
processing task, having three options:
Figure 3: Different possibilities for placing a processing task
They are not only suitable for periodic measurements for subsequent analysis of power usage
profiles, but can also be called at runtime by other simulated hardware or software components as
shown in Fig. 4. Examples of such simulated entities are batteries that update their state of charge or
energy-aware task placement strategies.
CMC, 2024, vol.81, no.3 3815
Figure 4: Complex application placement
•Placing the task on the sensor: Since the sensor does not have the computational capacities to
host a task that requires 3000 MIPS, this placement is not possible.
•Placing the task on the fog node: If the fog node still has enough remaining capacity, it can host
the task. This placement effectively reduces the amount of data sent via WAN.
•Placing the task in the cloud: The cloud has unlimited processing capabilities in this example.
Consequently, a placement is always possible.
LEAF supports online decision making based on power usage and complies with requirements as
shown in Fig. 5.
In theory, a power model can be any mathematical function and may depend on multiple input
parameters besides the load. This linear power model can be defined as:
P(t)=Pstatic +C(t)σ (1)
Evidently, this model improves the accuracy of the simulation. Nonetheless, when simulating
hundreds or thousands of nodes, more complex power models come at a cost. Calculating the
distance between that many interconnected devices at each time step of the simulation can become
computationally expensive. If the performance gets affected too much, an alternative approach, in
this case, could be to estimate an average distance and incorporate the energy dissipation directly into
the slope σof the linear model.
3816 CMC, 2024, vol.81, no.3
Figure 5: A linear power model is being followed in this research work
However, LEAF does currently not allow the modeling of asymmetrical bandwidth and power
consumption characteristics. Since edges in the infrastructure graph are undirected, network con-
nections are expected to provide the same bandwidth and consume the same power for uplink and
downlink-although, in reality, this is rarely the case. To obtain valid results, the user must determine
how the network is mostly used in the simulated scenario and choose an adequate parameter. For
example, if the mobile base stations in a simulation solely upload data to the cloud, the user should
pick a σ, which represents a realistic uplink consumption. A future version of LEAF can remedy this
deficiency by not utilizing an undirected graph as the basis for the infrastructure model but a directed
multigraph. This way, uplink and downlink can be modeled separately. Moreover, it would become
possible to have multiple different connections between two compute nodes. This would additionally
enable energy-aware routing algorithms that, for example, could decide whether it is better to send
data via Wi-Fi or Bluetooth.
Furthermore, for precise results, one would not only need to model the transmit power of base
stations but also how it degrades over the distance since mobile devices usually connect to the base
station with the strongest signal as given in Fig. 6. To summarize, if no further information is available
on the type and location of base stations or if different kinds of base stations are deployed, it is tough
to provide reasonable estimates on their consumption.
Figure 6: Topology of WAN between mobile device and cloud data center
CMC, 2024, vol.81, no.3 3817
If possible, the parameterization should be based on measurement data collected in the real
existing environment as given in Table 3. In fully conceived scenarios, the consumption of base stations
remains a significant factor of uncertainty.
Table 3: Power consumption of networking equipment
Type Model Max capacity Max power Energy per bit
Core router CRS-3 4480 Gbit/s 12300 W σcr =2.7 nJ/bit
Metro router 7603 560 Gbit/s 4550 W σmr =8.1 nJ/bit
BNG (Broadband Network Gateway) ASR 9010 320 Gbit/s 1890 W σbng =5.9 nJ/bit
Ethernet switch Catalyst 6509 256 Gbit/s 1766 W σes =6.9 nJ/bit
Data center switch Nexus 9500 102.4 Tbit/s 15954 W σdcs =0.2 nJ/bit
Incorporating these techniques into the fog environment simulation is of varying difficulty.
Shutting down hosts within compute nodes can be modeled by tracking how long they are idle and
marking them as powered-off after some time. For powered-off hosts, power models return P(t) =0. It
is significantly more complicated to switch off entire compute nodes or network links in the simulation
because this changes the network’s topology. Since the proposed model is relatively abstract and does
not model individual switches or routers, it is not suitable for modeling the dynamic shutdown of single
network devices.
4Results
To evaluate LEAF and present its capabilities, different architectures, and task placement strate-
gies were simulated in a smart city scenario. The results of these experiments are analyzed and discussed
in this chapter. Note that, the evaluation is not based on real measurement data-the simulated scenarios
are designed to be realistic but simple. Thus, the objective of this evaluation is not to provide exact
results on how to design future fog environments. It rather aims to demonstrate how researchers
can use LEAF to effectively analyze and improve the overall energy consumption of fog computing
environments. Furthermore, it will be shown that the model fulfils the requirements. The prototypical
implementation of LEAF is given in Fig. 7. The experimental setup of the evaluated scenarios explains,
analyzes, and discusses the results of the conducted experiments.
Figure 7: Live visualization of the prototype: (a) A map of the city, (b) the number of vehicles on the
map
3818 CMC, 2024, vol.81, no.3
Multiple modules were added or replaced in MATLAB (Matrix Laboratory). These changes are
not directly related to simulating fog environments but are general improvements to the library. In
particular, the MATLAB core implementation was improved in the following ways:
The existing network topology was highly coupled to BRITE topology files, and had an unintuitive
interface and a worst-case complexity for finding shortest paths, implementing only the Floyd–
Warshall algorithm. It was replaced with a more flexible interface which can be implemented by users
using libraries such as graphs to make use of the many features and algorithms implemented in this
library. The existing power models were only suitable for use with data center hosts. They were replaced
with a general power model that can be used for hosts and network links but is entirely decoupled from
their implementation.
The LEAF project itself depends on this patched MATLAB implementation core and contains
two packages: First, the package contains the proposed infrastructure and application model as well
as the related power models. The infrastructure and application graphs were implemented on top of
the graph with percentage of the distance between 1 to 1000 covered up to 98.75% using the LEAF
technique. Second, the package implements all functionality required in the evaluation. It contains the
city scenario, mobility model, compute nodes, network link types, and applications. Additionally, a live
visualization has been implemented with charts in order to provide insights into running experiments.
The visualization updates itself according to a user-defined frequency and contains four charts. 1. A
map of the city and the moving vehicles, 2. the number of vehicles that were present on the map at a
given time, 3. the infrastructure power usage over time and 4. the application power usage over time. An
example screenshot can be seen in the figure above. Each experiment run outputs two CSV (Comma
Separated Values) files containing the measurements. The analysis of the results was performed in
MATLAB.
First, all samples were grouped, based on the time of the day the passengers were picked up. The
time window is one minute; hence, there are 60.24 =1440 groups. Based on the frequency distribution,
the simulator generates new vehicles by periodically calculating the current probability of arriving cars
following a Poisson distribution. Fig. 8 shows the average number of vehicles generated per minute.
Figure 8: Number of vehicles generated per minute vs. average driving speed
CMC, 2024, vol.81, no.3 3819
We can observe that during early morning hours, only around five vehicles enter the map every
minute, while at the evening rush hour, it is up to 50. The vehicles arrive on one of the edges of the map
and follow a random path to another edge where they leave. The speed of the vehicles is determined
via the dataset. The median distance and duration of every group were used to calculate the average
speed at a given daytime. At night, vehicles can drive at more than 30 km/h; during the daytime, they
do not even reach 15 km/h.
A series of eight different experiments were conducted in the described smart city environment.
Table 4 below and Fig. 9 provide an overview of the results. The following sections explain all
experiments and analyze and discuss their outcome in detail.
Table 4: Aggregated energy consumption (Wh) of infrastructure components of all conducted experi-
ments
Experiment Total Cloud Fog (dynamic) Fog (static) WAN Wi-Fi
Cloud only 60,086 21,659 0 0 38,352 74
Fog 1 40,821 15,419 3239 2400 19,485 276
Fog 2 31,047 9841 6136 4800 9873 395
Fog 3 26,754 5147 8573 7200 5413 419
Fog 4 24,049 1517 10,458 9600 1950 522
Fog 5 24,431 98 11,195 12,000 606 530
Fog 6 26,689 0 11,246 14,400 513 529
Fog 6s 22,545 0 11,246 10,274 513 511
Figure 9: The above table is visualized as a stacked bar plot
To understand and improve on these results, we take a look at the causes of power usage. As
depicted in Fig. 10, all vehicular based applications together require a constant 1416.2 W of power,
totaling 56.5% of the overall energy consumption. Since LEAF uses an analytical approach for
3820 CMC, 2024, vol.81, no.3
modeling, it is straightforward to calculate where this power usage originates from 16 traffic light
systems constantly streaming 10 Mbit/s to the cloud over WAN, resulting in power usage of
16 ·10 Mbit/s·6685 nJ/bit =1069.6 W. (2)
Additionally, the processing tasks of these applications consume
16 ·30000 MIPS ·722 μW/MIPS =346.6 W. (3)
Figure 10: (a) Power usage of infrastructure in experiment Cloud, WAN and Wi-Fi, (b) Power usage of
applications in experiment Fog and Wi-Fi
The analysis of infrastructure and application power usage in experiment yielded that the many
deployed fog nodes impose a high static power usage on the system. Moreover, the fact that these fog
nodes are not always fully utilized is an evident inefficiency as given in Fig. 11. This inevitably raises
the question if fewer fog nodes that offload tasks to the cloud at peak times could lower overall energy
consumption. Further experiments were therefore conducted to demonstrate how LEAF can help to
plan more energy-efficient infrastructures.
Figure 11: Power usage of infrastructure in the experiment
CMC, 2024, vol.81, no.3 3821
The second difficulty is that the infrastructure topology is dynamic, and nodes are mobile. Wireless
connections have to be re-evaluated periodically to see what nodes are in range of each other. These
calculations can have a severe performance impact once there are many possible network connections,
for example, when vehicles are able to communicate with each other too. In this case, the number of
comparisons would raise with the number of vehicles on the map. One way to deal with this limitation is
by applying more advanced methods than linear search such as fixed-radius near neighbor algorithms.
Furthermore, domain knowledge can be applied to reduce the number of comparisons. For instance,
in the provided example, if a vehicle is very far away from another vehicle at a specific time step, there
is minimal time until these two vehicles may get in range of each other. No further comparisons need
to be made during this period as given in Tabl e 5 .
Table 5: The comparison of accuracy regarding the percentage of distance between 1 to 1000 with
existing literature
Article Technique Percentage of distance between 1 to 1000
[30] Artificial Neural Network (ANN) 97.97%
[31] Recursive Least Square (RLS) 96.28%
Proposed Large Energy-Aware Fog (LEAF) 98.75%
5Conclusion
In this research, an advanced LEAF based technique was introduced that allows for the modeling
of large-scale fog computing scenarios for the vehicular based network for executing thousands
of streaming applications on a distributed, heterogeneous infrastructure. LEAF features a holistic
but detailed energy consumption model that covers the individual infrastructure components and
the applications running on it. Unlike most existing simulators, which focus exclusively on energy
consumption of data centers or battery-constrained end devices, LEAF additionally takes the power
requirements of network connections into account. This is particularly relevant in the context of
IoT since mobile communication technologies such as 4G LTE or 5G are comparatively power-
hungry. From an environmental point of view, a general issue with mere technological efficiency
gains is that they are often canceled out by so-called rebound effects. The LEAF technique describes
that improved efficiency sometimes results in increased demand up to the point where the overall
effect can be detrimental. Cloud computing is a notable example of this, where energy efficiency is
improving at fast rates; nevertheless, the overall power required by cloud computing is rising every
year. Admittedly, improved energy efficiency is not the only reason for the increased demand for
cloud computing. A series of experiments were carried out on a prototypical implementation to show
that the model is useful and meets its stated requirements. The evaluation demonstrated that LEAF
enables the comprehensive analysis of the infrastructure’s power usage and can reveal the cause-effect
relationship between task placement and energy consumption. The integration of an adaptive energy-
saving mechanism illustrated how the energy consumption of fog computing environments can be
further reduced by consolidating workloads and switching off unused computing capacity and the
percentage of distance between 1 to 1000 was covered up to 98.75% using the LEAF technique.
However, some of the business models existing today, such as offering high-quality video streaming,
would certainly not be economical with less efficient hardware from 20 years ago. Instead of aiming
solely at further improvements in energy efficiency, an exciting direction for future research could be
3822 CMC, 2024, vol.81, no.3
focusing on the carbon emissions caused by fog computing infrastructures directly. Existing models
and simulators do not take into account the fact that the amount of green energy in the grid fluctuates
throughout the day. Google recently published an article on how they are planning to shift non-urgent
tasks, like processing YouTube videos, to times when renewable power sources provide most of their
energy. Similarly, a local lack or surplus of low-carbon energy could influence task placement and
workload scheduling in fog computing environments. Extending LEAF with additional data sources
like weather data, energy prices, and data on the carbon intensity of the current energy mix will enable
research on architectures and algorithms that save money and tones of CO2, rather than kilowatt-
hours.
Acknowledgement: The authors would like to acknowledge the support of Altinbas University,
Istanbul, Turkey for valuable support.
Funding Statement: The research received no funding grant from any funding agency in the public,
commercial, or not-for-profit sectors.
Author Contributions: Conceptualization, Sefer Kurnaz; methodology, Abduladheem Fadhil Khud-
hur; software, Ayça Kurnaz Türkben; validation, Abduladheem Fadhil Khudhur; formal analysis,
Abduladheem Fadhil Khudhur; writing—original draft preparation, Abduladheem Fadhil Khudhur.
All authors reviewed the results and approved the final version of the manuscript.
Availability of Data and Materials: The dataset was being acquired from KAGGLE which is open-
source https://www.kaggle.com/datasets/haris584/vehicular-obu-capability-dataset (accessed on 20
January 2023).
Ethics Approval: Not applicable.
Conflicts of Interest: The authors declare no conflicts of interest to report regarding the present study.
References
[1] V. Paidi, H. Fleyeh, J. Håkansson, and R. G. Nyberg, “Smart parking sensors, technologies and applications
for open parking lots: A review,” IET Intell. Transp. Syst., vol. 12, no. 8, pp. 735–741, May 2018. doi:
10.1049/iet-its.2017.0406.
[2] N. Alsbou, M. Afify, and I. Ali, “Cloud-based IoT smart parking system for minimum parking delays on
campus,” in Internet of Things–ICIOT 2019, 2019, pp. 131–139. doi: 10.1007/978-3-030-23357-0_11.
[3] T. N. Pham, M. -F. Tsai, D. B. Nguyen, C. -R. Dow, and D. -J. Deng, “A cloud-based smart-parking system
based on Internet-of-Things technologies,” IEEE Access, vol. 3, pp. 1581–1591, 2015. doi: 10.1109/AC-
CESS.2015.2477299.
[4] X. Wang and S. Cai, “An efficient named-data-networking-based IoT cloud framework,” IEEE Internet
Things J., vol. 7, no. 4, pp. 3453–3461, Apr. 2020. doi: 10.1109/JIOT.2020.2971009.
[5] M. Khalid, K. Wang, N. Aslam, Y. Cao, N. Ahmad and M. K. Khan, “From smart parking towards
autonomous valet parking: A survey, challenges and future works,” J. Netw. Comput. Appl., vol. 175, Feb.
2021, Art. no. 102935. doi: 10.1016/j.jnca.2020.102935.
[6] H. El-Sayed et al., “Edge of things: The big picture on the integration of edge, IoT and the cloud
in a distributed computing environment,” IEEE Access, vol. 6, pp. 1706–1717, 2018. doi: 10.1109/AC-
CESS.2017.2780087.
[7] A. Pozzebon, “Edge and fog computing for the Internet of Things,” Future Internet, vol. 16, no. 3, Mar.
2024, Art. no. 101. doi: 10.3390/fi16030101.
CMC, 2024, vol.81, no.3 3823
[8] A. Damianou, C. M. Angelopoulos, and V. Katos, “An architecture for blockchain over edge-enabled IoT
for smart circular cities,” in 2019 15th Int. Conf. Distrib. Comput. Sens. Syst. (DCOSS), Santorini Island,
Greece, May 2019. doi: 10.1109/dcoss.2019.00092.
[9] M. A. A. Ghamdi, “An optimized and secure energy-efficient blockchain-based framework in IoT,” IEEE
Access, vol. 10, pp. 133682–133697, 2022. doi: 10.1109/ACCESS.2022.3230985.
[10] V. K. Kukkala, J. Tunnell, S. Pasricha, and T. Bradley, “Advanced driver-assistance systems: A path
toward autonomous vehicles,” IEEE Consum. Electron. Mag., vol. 7, no. 5, pp. 18–25, Sep. 2018. doi:
10.1109/MCE.2018.2828440.
[11] W. A. A. Praditasari and I. Kholis, “Model of smart parking system based on Internet of Things,” Spektral,
vol. 2, no. 1, May 2021. doi: 10.32722/spektral.v2i1.3690.
[12] K. S. Awaisi et al., “Towards a fog enabled efficient car parking architecture,” IEEE Access,vol.7,pp.
159100–159111, 2019. doi: 10.1109/ACCESS.2019.2950950.
[13] C. Lee, Y. Han, S. Jeon, D. Seo, and I. Jung, “Smart parking system using ultrasonic sensor and Bluetooth
communication in Internet of Things,”KIISE Trans. Comput. Pract., vol. 22, no. 6, pp. 268–277, Jun. 2016.
doi: 10.5626/ktcp.2016.22.6.268.
[14] A. Shahzad, J. Choi, N. Xiong, Y. -G. Kim, and M. Lee, “Centralized connectivity for multiwireless edge
computing and cellular platform: A smart vehicle parking system,” Wirel. Commun. Mob. Comput.,vol.
2018, pp. 1–23, 2018. doi: 10.1155/2018/7243875.
[15] A. Shahzad, A. Gherbi, and K. Zhang, “Enabling fog-blockchain computing for autonomous-vehicle-
parking system: A solution to reinforce IoT-cloud platform for future smart parking,” Sensors, vol. 22,
no. 13, Jun. 2022, Art. no. 4849. doi: 10.3390/s22134849.
[16] W. Shao, F. D. Salim, T. Gu, N. -T. Dinh, and J. Chan, “Traveling officer problem: Managing car parking
violations efficiently using sensor data,”IEEE Internet Things J., vol. 5, no. 2, pp. 802–810, Apr. 2018. doi:
10.1109/jiot.2017.2759218.
[17] M. Y. Pandith, “Data security and privacy concerns in cloud computing,”Internet of Things Cloud Comput.,
vol. 2, no. 2, pp. 6–11, 2014. doi: 10.11648/j.iotcc.20140202.11.
[18] B. H. Krishna, S. Kiran, G. Murali, and R. P. K. Reddy, “Security issues in service model of cloud com-
puting environment,” Procedia Comput. Sci., vol. 87, pp. 246–251, 2016. doi: 10.1016/j.procs.2016.05.156.
[19] C. Stergiou, K. E. Psannis, B. B. Gupta, and Y. Ishibashi, “Security, privacy & efficiency of sustainable
cloud computing for big data & IoT,” Sustainable Comput.: Inform. Syst., vol. 19, pp. 174–184, Sep. 2018.
doi: 10.1016/j.suscom.2018.06.003.
[20] R. A. Memon, J. P. Li, M. I. Nazeer, A. N. Khan, and J. Ahmed, “DualFog-IoT: Additional fog layer for
solving blockchain integration problem in Internet of Things,” IEEE Access, vol. 7, pp. 169073–169093,
2019. doi: 10.1109/ACCESS.2019.2952472.
[21] M. Ma, G. Shi, and F. Li, “Privacy-oriented blockchain-based distributed key management architecture
for hierarchical access control in the IoT scenario,” IEEE Access, vol. 7, pp. 34045–34059, 2019. doi:
10.1109/ACCESS.2019.2904042.
[22] P. K. Sharma, N. Kumar, and J. H. Park, “Blockchain-based distributed framework for automotive
industry in a smart city,” IEEE Trans. Ind. Inform., vol. 15, no. 7, pp. 4197–4205, Jul. 2019. doi:
10.1109/TII.2018.2887101.
[23] V. K. Sarker, T. N. Gia, I. Ben Dhaou, and T. Westerlund, “Smart parking system with dynamic
pricing, edge-cloud computing and LoRa,” Sensors, vol. 20, no. 17, Aug. 2020, Art. no. 4669. doi:
10.3390/s20174669.
[24] Z. Liu, P. Dai, H. Xing, Z. Yu, and W. Zhang, “A distributed algorithm for task offloading in vehicular
networks with hybrid fog/cloud computing,” IEEE Trans. Syst., Man, Cybern.: Syst.,vol.52,no.7,pp.
4388–4401, Jul. 2022. doi: 10.1109/TSMC.2021.3097005.
[25] H. Cho, Y. Cui, and J. Lee, “Energy-efficient cooperative offloading for edge computing-enabled
vehicular networks,” IEEE Trans. Wirel. Commun., vol. 21, no. 12, pp. 10709–10723, Dec. 2022. doi:
10.1109/TWC.2022.3186590.
3824 CMC, 2024, vol.81, no.3
[26] S. Ghanavati, J. Abawajy, and D. Izadi, “An energy aware task scheduling model using ant-mating
optimization in fog computing environment,” IEEE Trans. Serv. Comput., vol. 15, no. 4, pp. 2007–2017,
Jul. 2022. doi: 10.1109/TSC.2020.3028575.
[27] M. Chaqfeh and A. Lakas, “A novel approach for scalable multi-hop data dissemination in vehicular ad
hoc networks,” Ad Hoc Netw., vol. 37, pp. 228–239, Feb. 2016. doi: 10.1016/j.adhoc.2015.08.021.
[28] U. M. Malik, M. A. Javed, S. Zeadally, and S. Ul Islam, “Energy-efficient fog computing for 6G-enabled
massive IoT: Recent trends and future opportunities,” IEEE Internet Things J., vol. 9, no. 16, pp. 14572–
14594, Aug. 2022. doi: 10.1109/JIOT.2021.3068056.
[29] L. Lu, T. Wang, W. Ni, K. Li, and B. Gao, “Fog computing-assisted energy-efficient resource allocation
for high-mobility MIMO-OFDMA networks,”Wirel. Commun. Mob. Comput., vol. 2018, no. 1, Jan. 2018.
doi: 10.1155/2018/5296406.
[30] M. Sookhak et al., “Fog vehicular computing: Augmentation of fog computing using vehicular cloud
computing,”IEEE Veh. Technol. Mag., vol. 12, no. 3, pp. 55–64, Sep. 2017. doi: 10.1109/MVT.2017.2667499.
[31] U. Farooq, M. W. Shabir, M. A. Javed, and M. Imran, “Intelligent energy prediction techniques
for fog computing networks,” Appl. Soft Comput., vol. 111, Nov. 2021, Art. no. 107682. doi:
10.1016/j.asoc.2021.107682.
Available via license: CC BY 4.0
Content may be subject to copyright.
