Enhanced Traffic Congestion Management with Fog Computing - A Simulation-Based Investigation using IFOG-Simulator

International Journal of Computer Networks & Communications (IJCNC) Vol.16, No.3, May 2024
DOI: 10.5121/ijcnc.2024.16303 29
ENHANCED TRAFFIC CONGESTION MANAGEMENT
WITH FOG COMPUTING - A SIMULATION-BASED
INVESTIGATION USING IFOG-SIMULATOR
Alzahraa Elsayed, Khalil Mohamed, Hany Harb
Systems and Computers Engineering Dept., Faculty of Engineering, Al-Azhar
University, Nasr city, Egypt.
ABSTRACT
Abstract: Accurate latency computation is essential for the Internet of Things (IoT) since the connected
devices generate a vast amount of data that is processed on cloud infrastructure. However, the cloud is not
an optimal solution. To overcome this issue, fog computing is used to enable processing at the edge while
still allowing communication with the cloud. Many applications rely on fog computing, including traffic
management. In this paper, an Intelligent Traffic Congestion Mitigation System (ITCMS) is proposed to
address traffic congestion in heavily populated smart cities. The proposed system is implemented using fog
computing and tested in a crowdedCairo city. The results obtained indicate that the execution time of the
simulation is 4,538 seconds, and the delay in the application loop is 49.67 seconds. The paper addresses
various issues, including CPU usage, heap memory usage, throughput, and the total average delay, which
are essential for evaluating the performance of the ITCMS. Our system model is also compared with other
models to assess its performance. A comparison is made using two parameters, namely throughput and the
total average delay, between the ITCMS, IOV (Internet of Vehicle), and STL (Seasonal-Trend
Decomposition Procedure based on LOESS). Consequently, the results confirm that the proposed system
outperforms the others in terms of higher accuracy, lower latency, and improved traffic efficiency.
KEYWORDS
Index Terms: Fog Computing; Traffic Congestion; Traffic Index; Cloud Computing; Traffic Sensing
Systems; iFog-Simulator.
1. INTRODUCTION
The number of connected devices globally is projected to reach 55.9 billion by 2025, with 75% of
them connected to IoT platforms, according to a report by the International Data Corporation
(IDC). IDC also predicts that the data generated by IoT devices will increase from 13.6 ZB in
2019 to 79.4 ZB by 2025. Smart cities rely on real-time or contextual data analysis to achieve
consistent results through collaboration across various sectors. However, the growth of motor
vehicles in urban areas has created traffic. pressures and issues, leading to the development of
traffic signal control using Information and Communication Techniques (ICT) [1, 2]. Cisco has
stated that current cloud models are inadequate for handling the velocity, diversity, and volume of
data generated by the IoT [3]. The growth of urban population, urbanization, and IoT applications
presents significant challenges, requiring the use of the latest technological advancements to
make cities smarter.[4].
Existing control strategies must overcome long decision and response latency from data
processing. The challenge for centralized computation infrastructure is to instantaneously respond
to adaptive signal control systems.

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Cloud computing environments provide storage and processing power to alleviate the burden on
local systems[5, 6] However, relying solely on the cloud computing model has some drawbacks.
For instance, data transmission to cloud servers may require wider network bandwidth, and the
service latency may increase as well [7-9]. These drawbacks can be critical issues for applications
such as security systems. To address these problems, researchers have recommended two models:
fog computing and edge computing[10].
In several IoT applications, analysis must be conducted instantly, and preventive action must be
taken immediately[11]. This means that the opportunity to prevent potential damage decreases
during data transmission from the device to the cloud for analysis. Cisco has published a report
on this challenging issue that disrupts IoT systems [12]. As the report indicates, IoT systems
require a new computing model to deal with the variety, rapidity, and quantity of IoT data.
[13]Therefore, instead of using centralized clouds, fog computing uses decentralized resources at
the network's edges to process data that is closer to the users [14, 15].
Fog computing, a novel and emerging model aims to minimize latency, conserve network
bandwidth, address security concerns, improve reliability, and ensure secure data collection
across a large geographic area with varying environmental conditions. It also involves moving
data to the optimal location for processing, distinguishing it from edge computing, which refers
to communication, processing power, and storage in end devices [8, 16].
Fog computing is considered a superior security model to cloud computing as it enables local
data analysis and offers the benefit of saving network bandwidth, resulting in reduced operational
costs. Additionally, the fog computing model excels in making rapid decisions and minimizing
arrival time[17]. In contrast, cloud computing often experiences longer data transfer times,
leading to increased latency and a higher risk of data loss. Consequently, the fog computing
model emerges as a more effective solution compared to cloud computing.
As shown in Figure. 1, fog computing acts as the middle layer between devices and the cloud.
The fog computing model for the Internet of Things (IoT) operates at the network edge to bring
processing and networking closer to users and data sources, thereby reducing latency and
improving processing and communication efficiency[18, 19].
This model is applicable in various applications, such as IoT, mobile applications, geographically
distributed applications, delay-sensitive real-time applications, crossroads applications, and
greenhouse applications. A fog node can be any device that has computing, storage, and network
connectivity [20, 21].
Figure 1: An IoT Fog Computing Architecture

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Smart traffic lights are an example of how integrating information and communication
technologies can improve the quality and efficiency of public services. Real-time transportation
data is uploaded to the fog to provide transport officials and consumers with updates on the city's
transportation availability and conditions[22, 23].
As the urban population grows, effective solutions are required to overcome transportation
problems and reduce traffic congestion. To reduce congestion in high-traffic cities, an open fog
computing model-based traffic control system is preferable to cloud computing [23]. This is
because with fog computing, data is analyzed locally, making it a more secure and cost-effective
option that saves network bandwidth. Additionally, fog computing can make decisions more
quickly, reducing arrival time. Conversely, cloud computing takes longer to transfer data,
increasing latency and the risk of data loss.
This paper introduces an Intelligent Traffic Congestion Mitigation System (ITCMS) that utilizes
fog computing to address the issue of traffic congestion in densely populated smart cities. Fog
computing extends the capabilities of cloud computing by processing data at the network edge,
reducing communication bandwidth and enabling real-time and Internet of Things (IoT)
applications. It offers several advantages, such as high processing speed, enhanced security and
privacy, and location awareness. To ensure reliable connectivity, collaboration between IoT
devices, platforms, and network providers is crucial in establishing stable connection points
within the fog network.
The ITCMS is installed on traffic lights and comprises a camera and three LEDs. The camera
detects crowded and non-crowded roads, enabling informed decisions regarding vehicle
movement. The proposed system is implemented within an environment consisting of four roads
(x, y, z, and w) where thesimultaneous presence of multiple cars leads to traffic congestion. Each
fog node is equipped with one camera and three LEDs (Yellow, Green, and Red). As a car travels
from R1 (source) to R2 (destination), the LED in the fog node corresponding to R2 turns green,
while the LEDs in the other roads turn red.
The main contribution of this paper is:
 The proposed solution for mitigating traffic congestion in densely populated urban
areas involves the implementation of the Intelligent Traffic Congestion Mitigation
System (ITCMS).
 The proposed ITCMS has been evaluated for its effectiveness, and the results have been
analyzed to demonstrate its efficiency. The findings suggest that the ITCMS can
significantly enhance traffic efficiency, conserve energy, and reduce latency, the
average traffic flow rate, and waiting time.
 The system performance of the proposed system is evaluated based on four critical
parameters: CPU usage, heap memory usage, throughput, and the total average delay.
These parameters are essential in determining the system's performance. Moreover, a
comparison has been drawn between the ITCMS and the recently implemented systems
for solving the same problem, namely IOV and STL, using two parameters: throughput
and the total average delay. This comparison provides valuable insights into the
strengths and weaknesses of each system.
The remainder of the paper is structured as follows: Section II discusses related works, while
Section III presents the mathematical framework of the proposed (ITCMS). Section IV outlines
the structure of the ITCMS, while Section V details its implementation. Lastly, in Section VI, the
simulation results are discussed, and the paper is concluded and summarized in Section VII.

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2. RELATED WORK
Given the significant impact of traffic congestion on people's daily lives, it is imperative to
address this issue effectively. Numerous efforts have been made to develop traffic management
strategies, specifically focusing on the creation of smart traffic signal systems aimed at
alleviating traffic congestion. Qin and Zhang [24] proposed a system that uses intelligent traffic
lights in fog computing to reduce and manage traffic congestion. This computer platform
employs a Q-learning algorithm to control real-time traffic flow by leveraging the capabilities of
fog computing for data processing.
Based on categorization and forecasting approaches, Muntean [25]offers a multi-agent system
(MAS) strategy for intelligent urban traffic management. They recommend using the k-nearest
neighbor and random tree classification models to forecast traffic flow with the greatest accuracy.
Golhar and Kshirsagar [26] suggested a system that combines video monitoring and a big data
analytics algorithm to regulate traffic. It introduces a traffic event detection framework.
Gupta, et al. [27] proposed a system that connects vehicles and traffic lights directly via the
Internet of Things (IoT), where vehicles provide real-time information to the traffic controller
regarding road transportation. However, their system is impractical due to its high cost, as each
vehicle must have an IoT device.
Rathore, et al. [28]proposed a system model that monitors traffic congestion in real time and
tracks any driving violations. The proposed system relies on a camera installed on the back of the
vehicle to monitor all preceding vehicles. The fog device connected to the car's camera detects
the violation and sends it to the authorities. However, their system is also impractical and costly.
Cunha, et al. [29] employed wireless technology to design a traffic management system, creating
a model that uses XBEE and a microcontroller connected to several wireless sensors. Brizgalov,
et al. [30] proposed a traffic control system model utilizing cloud computing. Perumalla and
Babu [31] developed a model that uses an IoT board, microcontroller board, and GPS module.
Rizwan, et al. [32] presented a system that uses IoT to collect and process traffic data.
The implementation of fog computing in a wide range of practical applications is gaining
momentum. Mohammed, et al. [33]propose a fog computing-based pseudonym authentication
(FC-PA) scheme for 5G-enabled vehicular networks. The scheme provides support for batch
signature verification, privacy preservation, and pseudonym authentication. It uses a single scalar
multiplication operation of elliptic curve cryptography for data verification. The FC-PA scheme
is designed to be secure under the random oracle model and is resilient against common security
attacks.
Al-Mekhlafi, et al. [34] propose a fog computing technique for 5G-enabled vehicle networks that
utilizes the Chebyshev polynomial. This scheme enables the revocation of pseudonyms and
employs fog computing to generate security parameters and validate the authenticity of vehicles.
An efficient mutual authentication scheme is proposed by Al-Shareeda and Manickam [35] for
5G-enabled vehicular fog computing, specifically in the context of COVID-19. The scheme has
two distinct aspects that rely on a designated flag value to differentiate between regular vehicles
and those associated with COVID-19.
Mohammed, et al. [36]present ANAA-Fog, an anonymous authentication scheme designed
specifically for 5G-enabled vehicular fog computing. This scheme aims to address privacy and
security concerns related to real-time services in the context of smart transportation. ANAA-Fog
leverages a fog server to generate temporary secret keys for vehicles participating in the system,
thereby ensuring the authentication and integrity of exchanged messages.

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Al-Mekhlafi, et al. [37]present an efficient authentication scheme for 5G-enabled vehicular
networks that leverages fog computing. The scheme is designed to tackle privacy and security
concerns while simultaneously minimizing communication and processing costs. A key
component of the scheme is the introduction of a fog server, which is responsible for establishing
public anonymity identities and generating corresponding signature keys for authenticated
vehicles.
However, most works focus on resource sharing among vehicles, with relatively little emphasis
on traffic signal control. Existing traffic control strategies that leverage fog computing primarily
aim to improve safety and efficiency, with limited attention given to traffic light optimization.
However, fog computing has promising potential for optimizing traffic phase timing due to its
low response latency, location awareness, and geographic distribution capabilities. Future studies
will focus on designing a scheme with better scalability and compatibility. This scheme incurs
slightly higher communication and computing costs than similar studies, but it demonstrates
effectiveness in achieving privacy and security objectives.
In this paper, we propose an architecture for traffic signal optimization based on fog computing,
which can be readily implemented in real-world scenarios.
.A comparison between previous works was concluded in Table 1
Table 1: Recent developments in traffic control systems.
Techniques Year Methodology Advantages The drawbacks
Using fog Computing
platform in data control
of real-time traffic flow
in Intelligent traffic light
[17].
2021 To propose an
Intelligent traffic light
(ITL) control technique,
use the Q learning
algorithm with fog
computing.
High performance
& Efficiency.
Priority was not
given to
emergency
vehicles.
Data from wireless
sensor networks are used
in a multi-agent system
for intelligent urban
traffic control. [18].
2022 Forecasts traffic flow
with highest accuracy
using k-nearest
neighbor and random
tree classification
algorithms.
Improving inter-
agent
communication.
Cannot handle
circumstances
such as
ambulances
going by, VIP
visits, and other
catastrophes.
Efficient Big Data
Analytics-Based Traffic
Management Strategies
[19].
2022 The framework traffic
event detection system
is introduced.
Big data should be
managed efficiently.
There is no
adaptive traffic
signal
management.
Smart Traffic Light
System Based on IoT for
Smart Cities [20].
2021 A smart system that
connects vehicles and
traffic lights via cloud
computing is proposed.
Minimize the
amount of
complexity.
Lane changes
are not
considered.
Smart traffic control:
Identifying driving-
violations using fog
devices with vehicular
cameras in smart cities
[21].
2021 proposed a system
model that monitors
traffic congestion in
real-time and tracks any
driving violations.
Detect the violation
and sends it to the
authorities.
Their system is
also impractical
and costly
Traffic Lights Control
Prototype Using
Wireless Technologies
[22].
2016 Employed wireless
technology to design a
traffic management
system.
Uses XBEE and a
microcontroller
connected to several
wireless sensors.
The model is
complex and
expensive.

34
Techniques Year Methodology Advantages The drawbacks
Architecture of traffic
control systems using
cloud computing [23].
2010 Traffic control system
model utilizing cloud
computing is proposed
can aid in the
resolution of motor
transport-related
issues such as
pollution, gridlock,
auto theft, and road
safety.
The use of
cloud
computing
makes the
system
insecure.
An intelligent traffic and
vehiclemonitoring
system using internet of
things architecture [24].
2016 Developed a model that
uses an IoT board,
microcontroller board,
and GPS module.
vehicle spotting,
and VIP and
emergency vehicle
clearance.
lack of
emphasis on
traffic signal
control.
Real-time smart traffic
management system for
smart cities by using
Internet of Things and
big data [25].
2016 Uses IoT to collect and
process traffic data is
presented
A mobile
application is
created as a user
interface to
investigate the
density of traffic.
Emergency
vehicles were
not given
clearance.
FC-PA: fog computing-
based pseudonym
authentication scheme in
5G-enabled vehicular
networks [26].
2023 Presents a fog
computing-based
pseudonym
authentication (FC-PA)
system for reducing
performance overhead
in 5G-enabled vehicle
networks.
efficiency and
security.
Higher
communication
and computing
costs.
Chebyshev polynomial-
based fog computing
scheme supporting
pseudonym revocation
for 5G-enabled vehicular
networks [27].
2023 a fog computing
strategy for 5G-enabled
automotive networks
that is based on the
Chebyshev polynomial.
Improved
performance and
cost effectiveness.
Not concerned
about privacy or
security.
COVID-19 vehicle based
on an efficient mutual
authentication scheme
for 5G-enabled vehicular
fog computing [28].
2022 Presents a COVID-19
vehicle for 5G-enabled
vehicular fog computing
based on an effective
mutual authentication
system.
More effective in
terms of
transmission and
computing.
Inaccurate and
higher cost.
ANAA-Fog: A Novel
Anonymous
Authentication Scheme
for 5G-Enabled
Vehicular Fog
Computing [29].
2023 Propose ANAA-Fog, an
anonymous
authentication system
for 5G-enabled vehicle
fog computing.
Unlink ability,
traceability, and
conditional privacy
preservation.
Higher costs for
connectivity
and computing
performance.
Efficient authentication
scheme for 5G-enabled
vehicular networks using
fog computing [30].
2023 A fog server is
employed in the
proposed FC-CPPA
approach to create a set
of public anonymity.
Criteria for privacy
and security.
Privacy and
security
objectives were
not taken into
account.
3. A MATHEMATICAL FRAMEWORK OF ITCMS
In this section, we will discuss the mathematical calculations used to compute the green time in
traffic signals for each road. We employ light equations in a real-time application to minimize
processing time. Eq. (1) is used to calculate the total number of vehicles, while Eq. (2) is used to
calculate the time for a single cycle. In Eq. (2), 𝑵𝒕𝒄is the total number of vehicles across all

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roads, 𝑵𝒄is the number of vehicles on each road, and 𝑻𝒕is the total time required to complete a
single cycle of traffic lights. The time required for each vehicle to cross the traffic light, µ, is 2.5
seconds in the simulation results.
𝑁𝑡𝑐 = ∑ 𝑁𝑐
8
𝑐=1 (1)
𝑇𝑡 = µ ∗ 𝑁𝑡𝑐 (2)
𝑘 =
𝑁𝑐
𝑁𝑡𝑐
(3)
𝑇𝑟 = 𝑘 ∗ 𝑇𝑡 (4)
To determine the green time in traffic signal on a specific road, we need to make the traffic light
more reliable using Compensation Equations (3) and (4). In Equation (3), k represents theratio
between the total number of vehicles on the roads and the number of vehicles on a particular
road. In Equation (4), Tr represents the green time at a particular road.
4. ITCMS STRUCTURE
Our research investigates the proposed Intelligent Transportation and Congestion Management
System (ITCMS), which aims to control traffic and alleviate congestion effectively. To showcase
the optimization of traffic flow, we focus on a transportation-related example that demonstrates
the simultaneous operations of fog nodes. The ITCMS is meticulously designed and implemented
using iFogSim, incorporating various essential modules, including:
1. The Fog Device Module: This module is responsible for creating all fog devices and
defining their hardware properties. These properties include device ID, MIPS (million
instructions per second), RAM, uplink bandwidth, downlink bandwidth, level, rate per
MIPS (cost rate per MIPS used), busy power (amperage rate per MIPS used), and the
power consumption when the fog node is idle.
2. The Sensor Module: This module is used to create the required IoT sensors. The sensor
can be connected to a router, fog node, or proxy via the gateway device. The setup link
latency represents the time required to establish a connection between the sensor and the
fog device. In the proposed ITCMS, a smart camera with two modules is utilized:
 Picture-capture module: Integrated into the smart camera, this module captures
images after a five-second delay, which are then transferred to the fog node.
 Slot detector module: This module identifies empty traffic light slots.
3. The Actuator Module: This module generates objects that display output information. In
the ITCMS scenario, LEDs serve as actuators to visually indicate the status of vacant
traffic light slots (red LED, green LED, and yellow LED). Actuators need to be connected
to a gateway device, through which data is transmitted. Therefore, when configuring
actuators in iFogSim, it is necessary to specify the gateway device and the latency of the
link.
The ITCMS comprises the following components:
 A cloud server
 Fog nodes
 Smart camera

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 Three LED display screens
The smart camera is positioned near the traffic lights to capture images of vehicles, which are
then transmitted to the fog node. On the fog node, an image processing method is implemented to
identify vacant slots near the traffic lights. Once the vacant slots are detected, the relevant
information is updated on the LED screens. The data is temporarily stored in the fog node before
being transmitted to the cloud server for permanent storage. This enables drivers to promptly
identify available spots upon reaching the traffic lights and move their vehicles to the designated
location.
The information displayed on the three LED screens is refreshed every five seconds. To facilitate
communication between the fog node and the cloud server, a proxy server is employed.
By employing this comprehensive system architecture, our proposed ITCMS aims to provide an
intelligent and efficient solution for traffic congestion management.
Figure 2: ITCMS based Intelligent traffic congestion mitigation.
5. THE PROTOTYPE OF THE ITCMS IMPLEMENTATION
The implementation of the proposed Intelligent Transportation and Congestion Management
System (ITCMS) prototype takes place in an environment consisting of four roads: x, y, z, and w.
In this configuration, the presence of multiple cars simultaneously results in traffic congestion.
This setup is visually depicted in Figure. 3. Each fog node is equipped with a camera and three
LEDs: Yellow, Green, and Red.
As a car moves from the source point (R1) to its destination (R2), the corresponding LED in the
fog node illuminates green, indicating the availability of a clear path. Meanwhile, the LEDs on
the other roads display red signals, indicating that those roads are congested. A detailed

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description of the proposed system can be found in Algorithm 1, providing a comprehensive
understanding of its functioning and operation.
Figure 3. The four fog nodes for four roads
Algorithm 1: Path Function
Input: R1, R2, R3, R4 and Cameras
Output: Led1(Red), Led2(Yellow), Led3(Green)
Function: path (S, D)
S: Source
D: Destination
1: Path (X, Y)
2: {
3: Let led of R1 = R2 = R3 = R4 = Led3(Green),
4: if (X Y)
5: Where X(car) in R1, Y(location) in R2
6: Then
7: Led of R1 = Led3(Green), led of R2=R3=R4 =Led1(Red)
8: else if (X W)
9: Where X(car) in R1, W(location) in R3
10: Then
12: else if (X Z)
13: Where X(car) in R1, Z(location) in R4
14: Then
16: end if
17: if (Y X)
18: Where Y(car) in R2, X(location) in R1
19: Then
21: else if (Y W)
22: Where Y(car) in R2, W(location) in R3
23: Then
25: else if (Y Z)
26: Where Y(car) in R2, Z(location) in R4
27: Then

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29: end if
30: if (W X)
31: Where W(car) in R3, X(location) in R1
32: Then
34: else if (W Y)
35: Where W(car) in R3, Y(location) in R2
36: Then
38: else if (W Z)
39: Where W(car) in R3, Z(location) in R4
40: Then
42: end if
43: if (Z X)
44: Where Z(car) in R4, X(location) in R1
45: Then
47: else if (Z Y)
48: Where Z(car) in R4, Y(location) in R2
49: Then
51: else if (Z W)
52: Where Z(car) in R4, W(location) in R3
53: Then
55: end if
6. SIMULATION RESULTS
To alleviate traffic congestion, this paper presents the application of ITCMS to Almohafza Street
in Mansoura, a city in Egypt with a population of approximately 6 million people. The
experiments were carried out using the NetBeans IDE version 8.2 software on a DELL Latitude
E6540 laptop. The iFogSim simulator, an open-source Java-based simulator developed by the
Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of
Melbourne {Mahmud, 2019 #31}, was used for the simulations.
The iFogSim simulation is built on the fundamental framework of CloudSim, which is widely
recognized as one of the most popular simulators for simulating cloud computing environments.
iFogSim extends the abstraction of core CloudSim classes to enable the simulation of a
customized fog computing environment, encompassing a multitude of IoT devices and fog nodes
such as sensors and actuators.
The evaluation and analysis of the performance of the ITCMS in this paper rely on four crucial
parameters: CPU usage, heap memory usage, throughput, and total average delay. These
parameters play a vital role in assessing the system's performance and determining its
effectiveness in alleviating traffic congestion. Furthermore, a comparative analysis has been
conducted, comparing the ITCMS with IOV and STL, using two key parameters: throughput and
total average delay. This comparison sheds light on the strengths and weaknesses of each system.
The ITCMS itself depends on several parameters, including latency, traffic efficiency, average
traffic flow rate, energy saving, and waiting time. The presented parameters in this paper are

39
specifically designed to accurately measure and analyze the performance of the ITCMS,
facilitating the identification of areas for improvement. By scrutinizing these parameters, it
becomes feasible to evaluate the system's efficacy in reducing traffic congestion and enhancing
traffic flow in densely populated cities:
Figure 4. The Simulated ITCMS for four roads
1- Cloud node 2- Proxy node
Name Cloud
Level 0
Uplink BW 1000
Downlink BW 1200
MIPS 500
RAM 45000
Rate/MIPS 1000
Name Proxy
Level 1
Uplink BW 1000
Downlink BW 1100
MIPS 4000
RAM 4500
Rate/MIPS 500
3- Fog node 4- LED or Camera node
Name Fog node
Level 2
Uplink BW 800
Downlink BW 1000
MIPS 1000
RAM 3000
Rate/MIPS 400
Name LED or Camera
TYPE SENSORS
Distribution type Uniform
MIN 20
Max 100
Cloud Proxy, Latancy:200
Proxy fog nodes, Latancy:100Fog nodes camera or led, Latancy:50
To simulate this scenario in iFogSim, it is necessary to generate a new class within the
org.fog.test.Perceval package as depicted in Figure. 4. The FogDevice class facilitates the
creation of fog nodes with different configurations through the utilization of a constructor. The
provided code snippet below can be employed to generate heterogeneous fog devices.
Figure. 5 illustrates the CPU usage during the simulation, which lasted less than two minutes.
The simulation began at 2:35:00 PM (Egypt Time), and the CPU usage started at 0% and
gradually increased to 70% by 2:35:30 PM (Egypt Time). It then gradually decreased to 20% by
2:35:45 PM (Egypt Time) and ultimately reached 0% by 2:35:50 PM (Egypt Time).

40
Figure 5. The CPU usage
Figure 6. The Heap Memory Usage
Figure. 6 shows the heap memory usage during the simulation. The used heap initially starts at 0
MB and gradually increases until it reaches 10 MB at 2:35:30 PM (Egypt Time). The used heap
continues to increase until it reaches approximately 250 MB at 2:35:45 PM (Egypt Time).
Finally, the used heap stabilizes at around 240 MB at the end of the simulation.
To verify the results of this study, eight fog nodes were deployed at eight roads, as shown in
Figure. 7. Each road has a fog node with one camera and three LEDs. When a car travels from
Road R1 (source) to Road R2 (destination), the LED in the fog node for Road R2 turns green,
while the LEDs in the fog nodes for the other roads turn red.
Figure 7: The eight fog nodes for eight roads.
The CPU usage
The CPU usage metric provides valuable insights into the percentage of processing power
utilized for data processing and program execution on a computer, server, or network device at
any given time. This metric plays a crucial role in maintaining optimal performance and ensuring
efficient system operation.
In essence, the CPU usage metric offers real-time information regarding the current utilization of
processing power. This data enables the identification of potential bottlenecks and facilitates the
implementation of corrective measures to enhance performance.

41
During the simulation depicted in Figure. 8, which lasted less than two minutes, the CPU usage
was observed. The simulation commenced at 5:24:30 PM (Egypt Time) with an initial CPU usage
of 0%. Subsequently, it gradually increased to 78% by 5:24:20 PM (Egypt Time). Following that,
the CPU usage gradually decreased to 50% by 5:24:28 PM (Egypt Time) and eventually reached
0% by 5:24:30 PM (Egypt Time).
The spike in CPU usage observed at the beginning of the simulation can be attributed to the
initialization of the ITCMS system. During this phase, the system loads data, initializes
algorithms, and establishes connections with the fog nodes. Once the system is fully initialized,
the CPU usage decreases as the system enters a stable state.
The gradual decrease in CPU usage throughout the simulation can be attributed to the effective
utilization of resources by the ITCMS system. The system leverages a fog computing architecture
to distribute the processing load across multiple devices. This approach enables the system to
handle large data volumes without significantly impacting CPU usage.
The final decrease in CPU usage to 0% occurs upon the termination of the simulation. At this
point, the ITCMS system releases all resources and returns to a low-power state.
Figure 8: The CPU usage
A. The Heap Memory Usage
The Java heap is a dedicated memory space specifically designed to store objects instantiated by
applications running on the JVM. When the JVM is launched, a certain amount of memory is
allocated for the heap, and any objects created can be shared among threads as long as the
program is running. This memory space plays a critical role as it enables dynamic memory
allocation, allowing objects to be created and destroyed in real-time as needed by the program.
The heap space is an integral component of the JVM runtime environment, and its efficient
utilization is essential for ensuring optimal performance and minimizing memory-related issues
such as memory leaks or out-of-memory errors. In essence, the Java heap provides a dedicated
memory area for storing objects created by applications running on the JVM, facilitating efficient
memory management and dynamic memory allocation.

42
Figure 9: The Heap Memory Usage
In Figure. 9, the Heap Memory Usage during the simulation is depicted. The Used Heap initially
starts at 0 MB and gradually increases until it reaches 10 MB by 5:24:19 PM (Egypt Time).
Subsequently, the Used Heap continues to grow, reaching approximately 500 MB by 5:24:28 PM
(Egypt Time). Finally, at the end of the simulation, the Used Heap stabilizes at around 240 MB.
B. The Throughput
The system's throughput represents the rate at which cars pass through the intersection per
second. To compare the proposed ITCMS system with previous traffic light management
scenarios, the simulation utilized the IoV[38] and STL[39] systems.
In the IoV system, it is assumed that cars enter the road every 2.5 seconds, with a car size of 4.5
meters and a gap of 0.5 meters between them. This implies that the maximum number of cars on
a single road is 80, and for two roads, it is 160.
The STL system calculates the overall time for a single cycle using basic mathematical
computations. STL estimates that the traffic light would remain open for 30 seconds, with four
roads converging. If two cars arrive every 15 seconds and three cars depart every 6 seconds at
green lights on each road, the total time for a single cycle is (30*4=120 seconds). The average
number of cars arriving in the signal cycle is (90/15)*2=12. The number of cars exiting during
the 30-second green light period is (30/6)*3=15 cars. The only modification in this system is
extending the duration of the green traffic light by 16 seconds to prevent congestion.
Throughput was measured for each system using relevant formulas and algorithms. The results
for each system demonstrate the relationship between time and the number of cars crossing the
intersection.
Figure. 10 illustrates the number of cars passing through the road intersection per second using
the proposed ITCMS system. The ITCMS system achieves benchmark throughput values, as
mentioned in [7]. As depicted in the figure, IoV and STL exhibit lower throughput compared to
the ITCMS system. This is attributed to the ITCMS system's more accurate prediction of the
optimal waiting time for each road, enabling a more efficient utilization of the green light
duration.

43
Figure 10. The Throughput of the traffic intersection
The accuracy of the ITCMS system has been demonstrated because the number of crossing cars
exceeds the number of waiting cars, as indicated in the throughput. This means that the ITCMS
system is able to effectively reduce traffic congestion.
C. The Total Average Delay
Addressing delays in traffic management systems poses a challenging and delicate task that
necessitates a dynamic and speedy network, along with lightweight algorithms. While various
strategies and procedures exist to tackle traffic congestion, only a few of them consider
guarantees for overall average delay. The ITCMS System asserts its ability to achieve guaranteed
latency by receiving real-time stream data and utilizing fog computing to predict the optimal
waiting time. This fog-based algorithm eliminates the need to waste time gathering and
transmitting data to remote servers, resulting in faster decision-making processes.
The red traffic duration is set to enable cars from each road to cross the intersection. As depicted
in Figure. 11, even with a high number of cars in the simulation process, the ITCMS System
exhibits acceptable delay times. This is attributed to the appropriate selection of a 5-second
yellow light duration and the system's mobility. The total delay experienced by the ITCMS
System is 30% less than the required delay of the IoV system and 60% less than that of the STL
system. Consequently, the proposed ITCMS System claims to outperform IoV and STL in terms
of reducing the average delay per car.
The findings indicate that the proposed ITCMS system accurately computes the optimal waiting
time for each road, effectively extending the green light duration for a specific road as the
number of cars increases.
Figure 11. The total average delay

44
In summary, the results of our proposed system were compared with those of previous studies in
the literature, including the study conducted byMohammed, et al. [40]. Through this comparison,
it was observed that our system significantly outperformed the performance of the previous study
by a 70% margin. Specifically, while Mohammed, T. S. et al. reported a latency of 11 seconds,
our study demonstrated a much-improved latency of only 4 seconds. These findings highlight the
superior performance of our proposed system and the significant advancements it offers in
comparison to previous research in this field.
To further validate the findings of this study, we conducted simulations using three different fog
node deployments: 4, 8, and 14 fog nodes. Each fog node was strategically positioned on a single
road, as summarized in Table 2. Each road was equipped with a fog node, one camera, and three
LEDs. Table 2 presents the simulation results for four key metrics: (a) execution time (ET), (b)
application loop delay (ALD), (c) camera transmission time (CTT), and (d) total traffic flow
(TTFU).
Table 2. Simulation results.
NoFN ET ALD CTT TTFU
4 4,538 49.67 5 3184
8 12,102 51.10 5 12736
14 48,481 53.25 5 39004
Execution time refers to the total time that the code ofiFog-Simulator implements. TheExecution
time for 4 fog nodes equal 4,538 seconds,the Execution time for 8 fog nodes equal 12,102
seconds andthe Execution time for 8 fog nodes equal 48,481 seconds. Fig. 12 illustrates the
relationship between execution time and the number of fog nodes, showcasing three distinct
scenarios with 4, 8, and 14 fog nodes.
Figure 12: The execution time.
The application loop delay is the total duration that it takes for the workflow program to execute
from the moment it is requested in clouds environment or in the fog. The application loop delay
for 4 fog nodes equal 49.67 seconds, the application loop delay for 8 fog nodes equal 51.10
seconds and the application loop delay for 14 fog nodes equal 53.25 seconds. Fig. 13 depicts the
application loop delay for three different fog node configurations, encompassing 4, 8, and 14 fog
nodes.

45
Figure 13: The application loop delay
smart cameras contain a picture-capture module which captures images after a five-second delay,
which are then transferred to the fog node. The camera transmission time for three distinct fog
node 4, 8, and 14 fog nodes equal 5 seconds. Fig. 14 illustrates the camera transmission time for
three distinct fog node deployments, comprising 4, 8, and 14 fog nodes.
Figure 14. Camera transmission time
The study of the transportation of individual drivers and vehicles between two sites, as well as
their interactions with one another, is known as traffic flow. The total traffic flow usage for 4 fog
nodes equal 3184 seconds, the total traffic flow usage for 8 fog nodes equal 12736 seconds and
the total traffic flow usage for 14 fog nodes equal 39004 seconds. Fig. 15 depicts the total traffic
flow usage for three distinct fog node configurations, encompassing 4, 8, and 14 fog nodes.
Figure 15. Total traffic flow usage

46
7. CONCLUSION AND FUTURE WORKS
This paper presents a novel approach to alleviating traffic congestion by strategically deploying
fog nodes at traffic intersections. These fog nodes are tasked with gathering, analyzing, and
processing real-time traffic data. The findings demonstrate that congestion can be effectively
mitigated by leveraging the average traffic flow rate between fog nodes and latency. To assess
the performance of the proposed ITCMS system, four key metrics are employed: CPU usage,
heap memory usage, throughput, and total average end-to-end delay. A comparative analysis of
the ITCMS, IOV, and STL systems is also conducted using throughput and total average end-to-
end delay metrics to identify the strengths and limitations of each system. Future research
directions include implementing the proposed system in large and densely populated cities like
Cairo to further evaluate its effectiveness in reducing traffic congestion.
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AUTHORS
Alzahraa Elsayed received an MSc in Communications Engineering and Computers
Engineering from the University of Al Azhar, Egypt (2018), where she is currently
pursuing a Ph.D. in Communications Engineering from 2019 to 2022, her research
interests include fog computing, cloud computing, and internet of things (IoT)
technologies.E-mail: alzahraa.salah@azhar.edu.eg
Khalil Mohamed received a Ph.D. in robotics and control engineering from Al-Azhar
University, Egypt in 2019. He is currently an assistant professor at Systems and
Computers Engineering Department, at Al-Azhar University, Egypt.His research interests
include AI, Machine learning, Deep learning, Reinforcement learning, Robotics, Control
theory, Intelligent Control Systems, Automotive Control Systems, Robust Control,
Stochastic Control, Motion and Navigation Control, Traffic and Transport Control,
Predictive control, Optimal control, Mathematics, Optimization, Task assignment in multi-robot systems,
Task decomposition.E-mail: eng.khalil@azhar.edu.eg
Hany Harb received a B.Sc. degree in computers and control engineering from the
Faculty of Engineering, Ain Shams University, Egypt in 1978, and an M.Sc. degree in
computers and systems engineering from the Faculty of Engineering, Al-Azhar University,
Egypt in 1981. He also received a Ph.D. degree in computer science and an M.Sc. degree
in operations research (MSOR) from the Institute of Technology (IIT), USA in 1986 and
1987, respectively. He is a professor of software engineering in the System Engineering Department,
Faculty of Engineering, Al- Azhar University, Egypt. His research interests include artificial intelligence,
cloud computing, and distributed systems. E-mail: harbhany@yahoo.com

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