Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

Abstract

The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an Integer Linear Program (ILP). This integrates the future estimation metric to predict the future arrivals of the vehicles, searching the optimal routes. Considering the mobile nature of IOV, fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path. The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path. Thus, this work strongly supports its use in applications having stringent Quality of Service (QoS) requirements for the vehicles.

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PressScience
DOI: 10.32604/cmc.2023.034413
Article
Traffic Management in Internet of Vehicles Using Improved Ant Colony
Optimization
Abida Sharif1, Imran Sharif1, Muhammad Asim Saleem2, Muhammad Attique Khan3,
Majed Alhaisoni4, Marriam Nawaz5,6, Abdullah Alqahtani7,YeJinKim
8and Byoungchol Chang9,*
1Department of Computer Science, COMSATS University Islamabad, Pakistan
2Faculty of Computing, Riphah International University, Faisalabad, Pakistan
3Department of Computer Science, HITEC University, Taxila, Pakistan
4Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman
University, Riyadh, 11671, Saudi Arabia
5Department of Computer Science, UET Taxila, Taxila, Pakistan
6Department of Software Engineering, UET Taxila, Taxila, Pakistan
7Software Engineering Department, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz
University, P.O. Box 151, Al-Kharj, 11942, KSA
8Department of Computer Science, Hanyang University, Seoul, 04763, Korea
9Center for Computational Social Science, Hanyang University, Seoul, 04763, Korea
*Corresponding Author: Byoungchol Chang. Email: bcchang@hanyang.ac.kr
Received: 16 July 2022; Accepted: 02 February 2023
Abstract: The Internet of Vehicles (IoV) is a networking paradigm related to
the intercommunication of vehicles using a network. In a dynamic network,
one of the key challenges in IoV is traffic management under increasing
vehicles to avoid congestion. Therefore, optimal path selection to route traffic
between the origin and destination is vital. This research proposed a realistic
strategy to reduce traffic management service response time by enabling real-
time content distribution in IoV systems using heterogeneous network access.
Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO)
algorithm and formulated the path planning optimization problem as an
Integer Linear Program (ILP). This integrates the future estimation metric
to predict the future arrivals of the vehicles, searching the optimal routes.
Considering the mobile nature of IOV, fuzzy logic is used for congestion level
estimation along with the ACO to determine the optimal path. The model
results indicate that the suggested scheme outperforms the existing state-of-
the-art methods by identifying the shortest and most cost-effective path. Thus,
this work strongly supports its use in applications having stringent Quality of
Service (QoS) requirements for the vehicles.
Keywords: Internet of vehicles; internet of things; fuzzy logic; optimization;
path planning

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1Introduction
The Internet of Things (IoT) is a network of ubiquitous gadgets, including computers, tablets,
vehicles, and smartphones. Communication linkage between multiple gadgets, the IoT network creates
a heterogeneous network [1]. For example, the Internet of Automobiles (IoV) facilitates Internet-based
communication and data sharing between vehicles. However, traffic management remained the key
issue with IoV. Several heuristics and algorithms have been suggested to select the shortest route
between the source and the destination [2]. Among the multiple pathways between the source and
destination, the best one should be picked, taking time and distance into account. However, choosing
the best route without considering the dynamics of traffic features (such as traffic flow and vehicle
speed) is challenging [2].
The word “path planning” refers to finding the best and least congested routes. Path planning has
recently attracted the attention of experts working in the field of automobiles [3]. Numerous methods,
such as the potential field method, evolutionary algorithm, simulated annealing algorithm, particle
swarm optimization, fuzzy logic, and dynamic window approach, have been suggested for effective
path planning [4–9].
The Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time
Window (VRPTW) handle the issue of finding the best routes for more than 100 automobiles (CVRP).
In [9], VRPTW, a nonlinear problem, is solved using a Non-dominated Sorting Genetic Algorithm
(NSGA). When tackling the VRPTW, the Tabu Search (TS) performs significantly better than the
NSGA. A simple but effective hybrid Genetic Algorithm (GA) is suggested in [10]asasolution
to this issue. Using real-world data and domain knowledge in GA, the researchers developed a
brand-new heuristic standard for vehicle routing. A fuzzy routing solution has also been developed
for the location-routing problem in-vehicle networks [11]. Researchers in [12] developed a hybrid
route planning system for cars that are based on particle swarm optimization (PSO) and swarm
algorithm (SA). However, the approaches lack flexibility and resilience and are quite vulnerable to
local optimization. Dorigo [13] first introduced ACO in the early 1990s. This novel meta-heuristic
method determines the foraging behavior of ants [14]. This algorithm has emerged as an effective
evolutionary approach for handling complex optimization problems. The ACO solves challenges
such as vehicle routing, network shortest path finding, and secondary distribution well because of
distributed and parallel processing features. In [15], the authors obtained declined average waiting and
transit time by formulating the path planning issue in IoV as an ACO optimization. Compared with
the state-of-the-art methods, researchers in [16] suggested a distributed ACO for vehicular networks
and enhanced network performance.
Researchers in [17] proposed a new navigation strategy for mobile robots working in dynamic
situations by utilizing the ACO’s heuristic characteristics. The current swarm intelligence (SI) based
routing protocols handle one or two indicators simultaneously while determining the optimum path.
They do not consider the correlation between many indices. However, this search strategy is not
appropriate for the situation where it is necessary to optimize numerous indices or pathways. The
authors of [18] presented an ACO method based on fuzzy logic for path planning; however, they did not
account for the dynamic character of the IoV environment, such as the cars’ potential future arrivals.
The ACO’s resilience and powerful computational capability have attracted many applications for
automobile traffic management. The optimal path is not necessarily to be the shortest distance path.
Other factors include safety, a breathtaking view, reduced traffic, and a small number of junctions.
Currently, there is no practical method for quick and accurate planning of these dynamic pathways.

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The best path is derived using nonlinear optimization with fuzzy logic and the SI in [19], which presents
a novel routing protocol based on fuzzy ant colony optimization (FACO).
2Literature Review
The Vehicular Ad hoc Networks (VANET) communication model uses roadside equipment and
vehicle-to-vehicle communication to convey information between the cars [19]. However, decreasing
the latency and enhancing network performance between the cars is one of the problems with the
existing IoV [20]. Path planning algorithms, such as Dijkstra, are employed to regulate traffic, but
they do not function well in dynamic situations like those seen in automotive networks [21].
The researchers improved the optimization technique and path-planning model for the IoV [22].
They offer a model so exact that it considers the steering window constraint and integrates length,
energy calculation, and collision risk using an objective function. Based on this concept, they created
a nature-inspired ACO algorithm to choose the best course. This technique is referred to as the
Pheromone-Assisted Ant Colony System (AP-ACS) because it combines the alarm pheromone with
the conventional guiding pheromone. This pheromone warns ants away from inefficient locations,
preventing them from doing pointless searches and improving the efficacy of the search. Three
heuristic metrics have been devised specifically to give ants the extra information they need for path
planning in the interim. The authors conducted scale-based comparisons between AP-ACS and the
present methods in various real-world settings. Their research revealed that the AP-ACS beat similar
methods concerning accuracy, stability, and efficiency while handling constraints well. Due to ACO’s
drawbacks, such as sluggish convergence and the ease with which it gets locked in a local optimum, [23]
presented a better ACO algorithm-based vehicle path planning. To create a place for moving vehicles,
it utilizes the grid approach. In a local optimum, a hybrid ant colony of common and scout ants is
used to evade traps. By conserving elite ants for accelerating convergence, the pheromone process is
enhanced by raising the ants’ sensitivity to the best path. In an area with various barriers, the fastest
route with the lowest chance of collision must be designed. The outcomes showed that the method is
efficient and useful. According to the available IoV studies, there exists a need for a study that considers
dynamics such as potential congestion when determining the ideal path [24].
Considering the previously stated methods, this work demonstrates the use of fuzzy logic in
conjunction with ACO, which dynamically chooses the best vehicle route based on various indices.
The benefits of the enhanced ACO and the FL are merged to identify the optimal path from the origin
and destination using several ideal factors. Given the dynamic nature of IoV, the best path, in this case,
is the one whose variables fully satisfy all user expectations. The following are the main contributions
of this work:
•To solve the issue that the standard ACO is vulnerable to local optimization, an improved ACO
has been developed.
•It has suggested a novel future congestion-based ACO that estimates the dynamic nature of the
IoV and calculates the optimal path considering the future arrival of the vehicles.
The remaining portions of the article are structured as follows: The proposed method for planning
courses for cars is established in Section 4; Section 2 examines the ACO and fuzzy logic; Section 3
introduces the path guidance system for IoV; Section 4 concludes; and Section 5 includes sections on
future suggestions.

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3Problem Formulation
A directed graph G =(V, E, D) is used to describe the proposed network model, where V stands
for the number of cars in an IoV network, E stands for the collection of directed edges, and D stands
for the separation between the source and destination. Table 1 a list of the annotations is presented
that has been used.
Table 1: Summary of notations
Notation Description
τij Pheromone on edge ij
LkLength of the path chosen by ant k
ηij Concentration of pheromone
dij Distance between the adjacent nodes
Pk
ij(t) Probability of choosing node jby ant k
ρEvaporation rate
RResponse variable
3.1 Optimization Model
An ILP is used to formulate the path planning optimization problem in IoV. The ILP’s primary
goal is to choose the best routes with the least congestion and travel time. Mathematically,
min N
i=1N
j=1αijCij (1)
(i,j)∈(i,j)out (i)fmi,j=(i,j)∈(i,j)in (i)fmi,j,∀i,j∈E\{xm,zt}(2)
(i,j)∈(i,j)out (xm)fmi,j=(i,j)∈(i,j)in (zt)fmi,j=|fL|(3)
αij =1if Eij =1
0otherwise (4)
As stated in Eq. (1), the goal of the optimization issue is to ztthe cost in terms of congestion and
distance for designing a vehicle’s path. Each flow fmthat is a member of FLhas a unique source and
destination node, Cij denotes the cost of each edge (i, j), and Eqs. (2) and (3) outline the restrictions
related to the flow of conservation. Eq. (4) shows the choice of taking the path or not. The literature
frequently uses ACO to enhance the design of vehicle network paths. Finding the quickest path while
considering distance and congestion may be portrayed as the ant species’ foraging behavior. An arrival
time of backward information can be used to update the value of the pheromone Eij.
3.2 Ant Colony Optimization
The proposed ACO uses a backward and forward ant procedure to search the optimal locations
to reach the destination. The IoV technology, such as Road Side Unit (RSU), IoT gateways, and base
stations, are used to collect information such as the number of cars, density of cars, and car speed.

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Mathematically, the congestion on the road can be calculated as follows,
Ci=1−Sp
nvimaxsp
(5)
The above Equation represents the congestion on the network. The lower the value of Ci, the less
congested the road is, and less traffic density is considered. The spp stands for vehicle speed, the nvi
displays the number of vehicles parked along the edge of the road, and the maxsp is the top speed of
the vehicles.
Say the current node for an ant kis denoted by the letter i. Based on the distance between nodes
jand iand the quantity of pheromones on edge ij, the ant kwill travel from node I to an unvisited
node jat time t. When there are several unvisited nodes, it is more likely that ant kwill select node j,
as shown by:
Pk
ij(t)=⎧
⎪
⎨
⎪
⎩
Tij (t)α.nij (t)β
[Tis (t)]α.[nis (t)]β
0otherwise
,j∈allowedj(6)
where ηij is the fuzzy value on edge ij visibility, the amount of pheromone on edge ij is denoted by Tij;
allowedjdenotes the set of contender nodes from where the ant kcan select one, signifies the importance
(weight) of the heuristic data (visibility) in selecting a path, and denotes the importance (weight) of
concentration of pheromone in selecting a path.
3.3 ACO-Based Future Congestion System
The proposed future congestion metric also models the arrival of the vehicles in the future while
searching for the optimal paths. Mathematically, it can be written as,
ECk
i,j=Ck
i,j+1−Vk
i,j
Ck
i,j
(7)
where Ck
i,j denotes the capacity of the vehicle in the k lane. The Vk
i,j shows the vehicle in the kth lane
and Ei,j, represents the edge or route i, j. ECk
i,j represents the expected congestion on edge or route i j,
on the kth lane.
4Pheromone Update
The arrival time of the retreating ant updates the pheromone value of the edge (i,j).
Tij (t+1)=(1−ρ)Tij (t)+n
k=1Tk
ij (8)
where Tk
ij is the quantity of pheromones that ant kleft in current iterations; ndenotes the number of
ants; and the evaporation rate of pheromone is denoted by ρ;
δTk
ij =⎧
⎨
⎩1
sp+1
nvi
+1
ECk
i,j
0otherwise
,edge
(i, j)is selected by ant k(9)
where spindicates the vehicles’ speeds, nvi represents the number of vehicles, maxsp is the maximum
speed of the vehicles and ECk
i,jpredicts the future congestion on the kth lane.

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The proposed algorithm for traffic management is shown by Algorithm 1. Initially, the ants
are generated, and their position is located. After that, the vehicles acting as an ant calculates the
congestion using the Equation as seen in the algorithm step 7. After that, the expected congestion is
calculated as shown in step 8. Finally, the optimal route information is updated in the ant database.
Algorithm: ACO Algorithm for Traffic Management
1: Generate ants
2: for each iteration, do
3: Locate the positions of the ants
4: for each loop, do
5: for each ant, do
6: if Ant is functioning, then
7: 1. Calculate the congestion using Equation:
8: Ci=1−Sp
nvimaxsp
9: 2. Calculate the future congestion as:
10: ECk
i,j=Ck
i,j+1−Vk
i,j
Ck
i,j
11: 3. Path information updated in the ant database
12: end if
13: Next ant
14: Next loop
15: Update pheromone value
16: Next iteration
17: Select the optimal path
4.1 Fuzzy Logic
Lotfi A. Zadeh, in 1965, presented the idea of a fuzzy set derived from a common set. Instead of
the characteristic function, the membership function having a value of either one or zero defines the
fuzzy set [25]. Membership implies that each object has the probability of being an element of the set.
The membership values lie within [0, 1]. If the value of 4 memberships is 1, the item is an element of
the set; if it is 0, the object is not a member of the set. Assilian and Mamdani devised sophisticated
fuzzy logic processes in the 1970s, especially without rigorous models [26]. The fuzzy logic control
methodology uses conditional sentences in place of equations. Inference refers to deriving a rule that
needs characterization through a membership function. The truthfulness of every proposition can
be evaluated through inference. The mechanism of an elementary FL control system is explained in
Fig. 1.Fig. 1 illustrates three key steps for attaining FL control: fuzzification, rule evaluation, and
defuzzification.

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Figure 1: A fuzzy control system
4.2 Fuzzification
The implementation of every fuzzy logic control system might be done in terms of fuzzy rules,
like:
Rule 1: if N is q1 & M is p1, then R is g1
Rule 2: if N is q2 & M is p2, then R is g2
Where the fuzzy parameters gi, qi, and pi are described by membership functions; R is the response
variable; N and M are conditional variables. Considering that conditional variable are generally
computed as one, Mamdani presented the fuzzy procedure for any membership function and the two
rules: taking D2 and D1 as intervals, the conditional variable’s memberships were measured as μq2(n)
and μp2(m) for Rule 2, and as μq1(n) and μp1(m) for Rule 1, followed by the matching of the computed
values with the equivalent fuzzy variables.
4.3 Rule Evaluation
Regarding the rules, the control rules that satisfy N =nandM=m are connected as follows:
Rule 1: μ1 =μp1 (m) and μq1 (n);
Rule 2: μ2 =μp2 (m) and μq2 (n);
where the min function, μ1, and μ2, are used to calculate the truth degrees of rules 1 and 2, respectively.
The collection of all fuzzy subsets, gi(R), is used to represent the output of the FL control system
(g(R)) by utilizing D3 as the output variable interval. The membership μgi(R) of the output provides
the total of all memberships.
μg(R)=μc1(R)∗μc2(R)(10)
where, ∗, signifies the largest operation for evaluating Mamdani-type rules.
4.4 Defuzzification
The fuzzy output, or the result of inference, is converted into real values and used as an input for
the FL control system. Since a non-fuzzy result is necessary, it is possible to determine the quantitative
value of the FL control system output by μgi(R). The area of gravity (COA) procedure is generally used
ZCOA =μA(Z)dz
μA(Z)d(11)
Here, Z signifies the membership of all elements in the subset of fuzzy output as an integral on
the continuous domain Z. There are equal areas on both sides of the ZCOA .

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5Simulation Results
Using the Intel Core i7-4790 CPU running at 3.6 GHZ machine, MATLAB is installed to run
the simulations. Table 2 shows the simulation parameters. The average waiting and travel time are
measured in ticks. In the simulation setting, where there is a high vehicle density, the distribution of
the vehicles is random. The density of the vehicles ranges from 10% to 60% in each direction of the
simulation environment. The environment will be considered congested if the vehicle’s density is greater
than 60%. The evaluation for average waiting time of the vehicles is evaluated using three scenarios
in the IoV. 1) Single intersection: The vehicle at a given junction node decides to move in different
paths; 2) Multiple lanes at the intersection: a situation in which a single car travels down more than
one lane; 3) numerous intersections: a scenario in which a single vehicle travels through more than one
intersection to represent a large-scale network (Figs. 2–4).
Table 2: Simulation parameters
Simulation Parameters
Vehicle speed 7 to 15 patches/10 ticks
Vehicle acceleration 0.5 to 2.5 patches/10 ticks
Density of vehicles 10 percent to 60 percent
α1
β5
Figure 2: Average travel time

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Figure 3: Congestion plot
Figure 4: Future estimated congestion
Figs. 2 to 4show the membership functions of the proposed algorithm. The membership functions
significantly enhance the performance of the fuzzy representation. The membership function can
graphically represent the fuzzy set. The fuzzy rules for searching the optimal paths are based on the
inputs of travel distance, congestion, and future estimated congestion. The trapezoidal membership
function has been used to calculate the optimal paths for each input. Fig. 2 exhibits a trip distance’s
membership function, Fig. 3 the congestion’s membership function, and Fig. 4 the anticipated future
congestion’s membership function.
Fig. 5 shows the fuzzy logic system for the proposed algorithm. The inputs for the Fuzzy system
are the travel distance, congestion, and estimated future congestion based on the trapezoidal mem-
bership function. Finally, the defuzzification process is completed, and the value of the pheromone is
updated.

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Figure 5: Fuzzy logic control system of the proposed algorithm
The readability and clarity of road networks are crucial in path planning. However, the road
network information is either static or dynamic. It is impossible to digitize the dynamic information,
so it is rarely considered in evaluating paths. A novel concept named “Future Congestion” has been
suggested in this paper, as presented in Fig. 5. For an actual path with a Ln length, three dynamic
parameters, including the travel speed, congestion, and future expected congestion are integrated while
searching for an optimal path (Tabl e 3 ).
Table 3: Fuzzy rules for the parameters
Rule number Travel distance by
an ant
Congestion on
the edge
Future congestion
estimation
Local pheromone
update
1 >65% <35% <35% VS
2 >65% >65% <35% S
3 >65% <35% >65% S
4<35% <35% <35% S
5<35% <35% >65% W
6<35% >65% <35% W
2 >65% >65% >65% W
8<35% >65% >65% VW
Terms: Very Strong =VS, Strong =S, Weak =W, V e r y We a k =VW
Comparison of offline optimization technique with proposed ACO: The problem of searching for
the optimal path in a dynamic IoV network is formulated as an ILP. There are two approaches, one for
evaluating results for offline comparison and the other for evaluating results for online comparison.
The offline comparison is made using a proposed optimization technique, as discussed in Section 3.
ILP can search for the optimal paths as it considers that the network already has all the information.
On the other hand, the network dynamics cannot be calculated in advance, so online comparison takes
the current network states as input and searches for an optimal path. ILP finds the optimal paths as
compared to heuristics. It is clear in Fig. 6 that the proposed ACO has a moderate deviation from ILP

CMC, 2023, vol.75, no.3 5389
and is considered an effective approach for finding the optimal path, considering the time complexity
of the algorithms. The proposed approach considers the congestion, future congestion, and travel
distance for finding the optimal path and can be mathematically defined as:
P∗=k
i=1,j=1Di,j1+Ei,j+Ci1+Si,j (12)
Figure 6: Comparison of proposed ACO and integer linear program optimization with average travel
time
The simulation results show a decrease in path with the traffic flow. Among various factors, just
the road distance is considered in the actual path. However, the proposed approach considers the
congestion, FC, and travel distance, making it closer to the real conditions. Concerning the actual
path length, the ideal result is attained by the proposed algorithm in comparison with [19].
Average travel time: The suggested algorithm’s average journey time is displayed in Fig. 7 alongside
well-known techniques. The proposed approach beats the benchmark systems by 27.6% and 13.4%
when compared to the ACO [15]and[19], respectively, which incorporate virtual path length with
dynamic parameters when identifying the best path. As the number of cars rises, the average wait time
also increases. The proposed algorithm efficiently adapts to the dynamic situation of vehicles in an IoV
environment such as FC while finding the optimal path. The vehicles are connected, and based on the
collaboration among vehicles, the suggested technique can make dynamic and intelligent decisions,
especially in the case of higher vehicle densities. The connected vehicles can adaptively balance the
pheromone value based on actual traffic information. On the other hand, the benchmark scheme
does not consider the real conditions of the road while searching for the optimal path in terms of
waiting time.

5390 CMC, 2023, vol.75, no.3
Figure 7: Average travel time
Average Waiting Time: Fig. 8 illustrates the typical wait time. The suggested fuzzy-based ACO
strategy yields an average time slower than state-of-the-art methods. The suggested method computes
pheromone values using real-time traffic flow data. The recommended approach can reduce average
waiting times by adopting a virtual route length based on dynamic measurements and can produce
pheromone values based on exchanging information about real-time traffic flow. On the other hand,
the dynamic circumstances of the IoT network cannot be modeled by the benchmark systems.
Figure 8: Average waiting time
Optimal Path: The scheme is compared with the traditional ACO and the improved FLACO [19]
that considers dynamic path planning. The red line in Fig. 9 shows the classical ACO, the blue line
shows the improved ACO, and the black line shows the proposed future congestion-based estimation
technique while finding the optimal path from source S to destination D.

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Figure 9: Optimal path considering the future congestion
Fig. 9 shows the comparison of the proposed scheme with the benchmark techniques while
searching for the optimal path. The red line shows the path from S to D in ACO [15]. It performs well
in the case of static environments; however, its performance degrades if the nature of the environment
is dynamic. [19] It considers the dynamic nature and uses a fuzzy approach to find the optimal path.
Consequently, it does not consider future congestion while searching for optimal paths. The proposed
scheme considers the dynamic nature of the vehicles in the IoV while considering future congestion
while forwarding the optimal path. Thus, the proposed scheme performs best compared to benchmark
techniques.
6Conclusion
This paper presents a novel ACO-based technique that considers future congestion while searching
for optimal paths. The algorithm searches the paths while considering the expected future arrival of
cars in the network. The results show that our approach has optimized the network resources and
reduced the delay of the IoV network as it calculates the paths with a future estimation of congestion
on the paths. The execution of the proposed work is tested by running simulations on a road network
topology and comparing results with the improved ACO and the classic ACO. The proposed method
outperformed in finding the most cost-efficient route compared to the other two algorithms. In the
future, this approach can be optimized further (e.g., reduction in computing time), and its application
needs to be extended to planning paths for a group of vehicles.
Funding Statement: This work was supported by “Human Resources Program in Energy Technology”
of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial
resources from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20204010600090).
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
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