Challenges In Vehicular Ad Hoc Networks Research Articles

Optimization of Intelligent Transportation System using Biologically-Inspired Vehicular Ad hoc Networks for Achieve the Desired Performance

Many innovations made possible by the Intelligent Transportation System (ITS), such as media apps, encrypted financial transactions, and effective traffic management, rely heavily on vehicular ad hoc networks (VANETs). Using bio-inspired methodologies, This study looks back at the past and forward to the future to examine all of the routing challenges in VANETs, whether they are associated with a chain of related routing tasks or are aimed at a group of distinct approaches to routing. The high node mobility and unpredictable vehicle distribution (on the road) lead to major issues for VANETs, including the design of a network's physical architecture and unstable connections. VANET's provision of reliable and appropriate vehicular contact in situations requiring good service is crucial. As a result, effective means of navigation are desperately needed in VANET. Hence, in this paper, we examine the Bio-Inspired vehicular ad hoc networks (Bio-VANETs), wherein, should a suggested algorithmic network fail at any given node or vehicle, the remaining vehicles may be able to take over the task of relaying the data to the necessary nodes to achieve the desired performance. Route lifetime increases, and connection failures are decreased when the shortest way is selected using the fewest possible hops over highly connected links. In addition, the received signal intensity fluctuations due to vehicle density and speed are assessed. Packet Delivery Ratio, Optimal Performance, Accuracy and Efficiency of Bio-VANET are discussed and simulated against other methods that are existing models.

A novel of congestion control architecture using edge computing and trustworthy blockchain system

Recently, Vehicular Ad-hoc Network (VANET) has been one of the emerging fields of research. Many researchers are doing their research on various challenges of VANET. Congestion or blockage has become a critical issue in intelligent transportation systems, and this problem may arise daily due to the usage of smart technology in VANET. So we need some mechanism which controlscongestion. This paper present the trustworthy, long-lasting and consistent block chain congestion control mechanism using the heterogeneity of Dullening Nural Network (DNN), Q-Learning, and Software Define Network (SDN) model for an accurate result, fixed infrastructure, together with a correct prediction of congestion when it occurs at the edge of the network and give the fast and correct decision of congestion w.r.t VANET trust, Quality of service (QOS) and other vehicles current request. The focus of our research is on distributed SDN Technology and block chain technology for the development of smart cities and linked vehicles. So we proposed an inexpensive mechanism with low latency and a low bandwidth block chain system. Based on the Simulation result, our proposed architecture gives 82% and 98% reliability and efficiency gain in a congestion environment compared to traditional approaches. This paper aims to increase throughput, Packet Delivery Ratio (PDR), energy consumption time, and less end-to-end delay and routing overhead during communication.

Clustering, Routing, Scheduling, and Challenges in Bio-Inspired Parameter Tuning of Vehicular Ad Hoc Networks for Environmental Sustainability

Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely high node mobility and ambiguous vehicle distribution (on the road), VANETs degrade in flexibility and quick topology, facing significant issues such as network physical layout construction and unstable connections. These challenges make it difficult for vehicle communication to be robust, reliable, and scalable, especially in urban traffic networks. Numerous research investigations have revealed a nearly optimal solution to various VANET difficulties through the application of techniques derived from nature and evolution. On the other hand, as key productivity sectors continue to demand more energy, sustainable and efficient ways of using non-renewable resources continue to be developed. With the help of information and communication technologies (ICT), parameter tuning approaches can reduce accident rates, improve mobility, and mitigate environmental impacts. In this article, we explore evolutionary algorithms to mobile ad hoc networks (MANETs), as well as vehicular ad hoc networks (VANETs). A discussion of three major categories of optimization is provided throughout the paper. There are several significant research works presented regarding parameter tuning in cluster formation, routing, and scheduling of broadcasts. Toward the end of the review, key challenges in VANET and MANET research are identified.

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an 'Internet of vehicles' with the potential to significantly enhance citizens' mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.

Addition of Neighbors in the Number of Vanets Node Factors: DSR-PNT Performance Study

Vehicular Ad Hoc Networks (VANETs) are part of Mobile Ad Hoc Networks (MANETs). Vehicles that become a node in VANETs move quickly, and speed is variable to change the topology quickly. One of the most prevalent challenges in VANETs is vehicle connection and picking the most appropriate vehicle to act as an intermediate between the sender of the packet and the destination to decide the node. The most suitable technique for communicating nodes requires research in creating the most desirable nodes as forwarders. Vehicle speed, acceleration, the direction of movement, and vehicle quality are examples of these factors. Futures Total Weight Route (TWR) may determine the ideal route from source to destination if these three characteristics at each neighbor node are known. This study discusses the impact of adding a parameter—the neighbor node on the routing metric in determining the value of TWR. The contribution of this research is to improve the performance of data packet transmission by adding a neighbor node on a DSR-PNT in the selection of routing nodes (node forwarder, next hop) to increase the PDR (packet delivery ratio). TWR provides the reference parameter (Speed, Acceleration, Link Quality) most prioritized continuously according to the changing scenario and node condition.

Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control

In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same content are generated. Location-based dependency requirements make the system more congested. Content pre-caching is an existing challenge in VANET; pre-caching involves the content's early delivery to the requested vehicles to avoid network delays and control network congestion. Early content prediction saves vehicles from accidents and road disasters in urban environments. Periodic data dissemination without considering the state of the road and surrounding vehicles are considered in this research. The content available at a specified time poses considerable challenges in VANET for content delivery. To address these challenges, we propose a machine learning-based, zonal/context-aware-equipped content pre-caching strategy in this research. The proposed model improves content placement and content management in the pre-caching mode for VANET. Content caching is achieved through machine learning, which significantly improves content prediction by pre-caching the content early to the desired vehicles that are part of the zone. In this paper, three algorithms are presented, the first is zone selection using the customized algorithm, the second is the content dissemination algorithm, and the third is the content pre-caching decision algorithm using supervised machine learning that improves the early content prediction accuracy by 99.6%. The cache hit ratio for the proposed technique improves by 13% from the previous techniques. The prediction accuracy of the proposed technique is compared with CCMP, MLCP, and PCZS+PCNS on the number of vehicles from 10 to 150, with an improved average of 16%. Finally, the average delay reduces over time compared with the state-of-the-art techniques of RPSS, MLCP, CCMP, and PCZS+PCNS. Finally, the average delay shows that the proposed method effectively reduces the delay when the number of nodes increases. The proposed solution improves the content delivery request while comparing it with existing techniques. The results show improved pre-caching in VANET to avoid network congestion.

A Q-Learning and Fuzzy Logic-Based Hierarchical Routing Scheme in the Intelligent Transportation System for Smart Cities

A vehicular ad hoc network (VANET) is the major element of the intelligent transportation system (ITS). The purpose of ITS is to increase road safety and manage the movement of vehicles. ITS is known as one of the main components of smart cities. As a result, there are critical challenges such as routing in these networks. Recently, many scholars have worked on this challenge in VANET. They have used machine learning techniques to learn the routing proceeding in the networks adaptively and independently. In this paper, a Q-learning and fuzzy logic-based hierarchical routing protocol (QFHR) is proposed for VANETs. This hierarchical routing technique consists of three main phases: identifying traffic conditions, routing algorithm at the intersection level, and routing algorithm at the road level. In the first phase, each roadside unit (RSU) stores a traffic table, which includes information about the traffic conditions related to four road sections connected to the corresponding intersection. Then, RSUs use a Q-learning-based routing method to discover the best path between different intersections. Finally, vehicles in each road section use a fuzzy logic-based routing technique to choose the foremost relay node. The simulation of QFHR has been executed on the network simulator version 2 (NS2), and its results have been presented in comparison with IRQ, IV2XQ, QGrid, and GPSR in two scenarios. The first scenario analyzes the result based on the packet sending rate (PSR). In this scenario, QFHR gets better the packet delivery rate by 2.74%, 6.67%, 22.35%, and 29.98% and decreases delay by 16.19%, 22.82%, 34.15%, and 59.51%, and lowers the number of hops by 6.74%, 20.09%, 2.68%, and 12.22% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it increases the overhead by approximately 9.36% and 11.34% compared to IRQ and IV2XQ, respectively. Moreover, the second scenario evaluates the results with regard to the signal transmission radius (STR). In this scenario, QFHR increases PDR by 3.45%, 8%, 23.29%, and 26.17% and decreases delay by 19.86%, 34.26%, 44.09%, and 68.39% and reduces the number of hops by 14.13%, 32.58%, 7.71%, and 21.39% compared to IRQ, IV2XQ, QGrid, and GPSR, respectively. However, it has higher overhead than IRQ (11.26%) and IV2XQ (25%).

Improved Recurrent Neural Network Schema for Validating Digital Signatures in VANET

Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.

A Survey on Mobile Road Side Units in VANETs

The number of vehicles on the road increases daily, causing many fatal accidents and wasting much time for the average commuter every day due to congestion. Vehicular ad hoc networks (VANETs) were introduced to overcome these issues by enabling vehicle-to-vehicle communication and vehicle-to-infrastructure communication. The prime challenge in VANETs is the necessity of very low communication delays, especially for safety-related applications due to the high mobility nature of vehicles. The VANET architecture introduces a network component, the Road Side Unit (RSU), to meet the required delay limitations. Even though the RSU is a critical component in VANETs, as expected, the RSUs were not deployed throughout the world because of their high investment cost. As a solution, the idea of mobile RSU (mRSU) was introduced, and, ever since, several techniques of mRSU deployment strategies have been proposed. In this survey, we first analyze the importance of the RSU to the VANET architecture with real-world data incorporating the new 5G standard. Then, we investigate the research done in the areas of mRSU and exploit the pros and cons of each mRSU deployment strategy. Finally, we also discuss the future research directions of mRSU, and we explain the challenges connected to these future trends.

A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.

A novel approach to select cluster head and optimising the data transmission in VANET

Communication between the vehicles has a major role in intelligent transportation system (ITS). Vehicular ad hoc network (VANET) is developed to improve the road safety. Data transmission between the vehicles has the major challenge in VANET, because VANET has high mobility and dynamic topology. In VANET, the cluster based topology used to send the data from one vehicle to many vehicles without any data loss. Cluster head (CH) plays a vital role during the data transmission in cluster based routing. CH manages the cluster formation and data transmission in each cluster. In this paper, we proposed a cluster based approach to improve the data transmission. In this system a novel approach is proposed for choosing the CH. The CH selection algorithm is done in three levels, first level normal CH selection, second level is cluster leaving and finally cluster merging. After choosing the CH, the routing path to be established based on the link quality which is a measure using the link life time. The proposed system is implemented using the SUMO and NS3 tools.

Hybrid Genetic Firefly Algorithm-Based Routing Protocol for VANETs

Vehicular Adhoc Networks (VANETs) are used for efficient communication among the vehicles to vehicle (V2V) infrastructure. Currently, VANETs are facing node management, security, and routing problems in V2V communication. Intelligent transportation systems have raised the research opportunity in routing, security, and mobility management in VANETs. One of the major challenges in VANETs is the optimization of routing for desired traffic scenarios. Traditional protocols such as Adhoc On-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), and Destination Sequence Distance Vector (DSDV) are perfect for generic mobile nodes but do not fit for VANET due to the high and dynamic nature of vehicle movement. Similarly, swarm intelligence routing algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) routing techniques are partially successful for addressing optimized routing for sparse, dense, and realistic traffic network scenarios in VANET. Also, the majority of metaheuristics techniques suffer from premature convergence, being stuck in local optima, and poor convergence speed problems. Therefore, a Hybrid Genetic Firefly Algorithm-based Routing Protocol (HGFA) is proposed for faster communication in VANET. Features of the Genetic Algorithm (GA) are integrated with the Firefly algorithm and applied in VANET routing for both sparse and dense network scenarios. Extensive comparative analysis reveals that the proposed HGFA algorithm outperforms Firefly and PSO techniques with 0.77% and 0.55% of significance in dense network scenarios and 0.74% and 0.42% in sparse network scenarios, respectively.

TDMP: Reliable Target Driven and Mobility Prediction based routing protocol in complex Vehicular Ad-hoc Network

Vehicle-to-everything (V2X) communication in vehicular ad hoc network (VANET) has emerged as a crucial component in advanced Intelligent Transport System (ITS) for information transmission and vehicular communication. One of the vital research challenges in VANET is the design and implementation of novel network routing protocols which bring reliable end-to-end connectivity and efficient packet transmission to V2X communication. The organically changing nature of road traffic vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packets delivery. Therefore, position-based routing protocols tend to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. Therefore, a novel target-driven and mobility prediction (TDMP) based routing protocol is developed in this paper for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. To implement an effective routing protocol, TDMP primarily involves the destination target of a driver for the mobility prediction and Received Signal Strength Indicator (RSSI) for the inter-vehicular link-status estimation. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, the proposed TDMP is able to enhance the packet transmission with the consideration of the estimation of inter-vehicular link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout. Based on the extensive simulations carried out on operational road environments with varying configurations and complexity, the experimental results show better performance in terms of improving packet delivery ratio by 21-57%, reducing end-to-end delay by 13-47% and average hops count by 17-48% in comparison with several typical position-based routing protocols, such as GPSR, GyTAR and PGRP.

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

Towards Identity-Based Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have become increasingly common in the past decades and provides essential and efficient communication for vehicles within intelligent transportation systems. Securing the VANETs wireless communication channel is one of the principal challenges in VANETs since existing security schemes are still vulnerable to security and privacy issues and have substantial computational and communicational overheads. To overcome these issues, this paper focuses on enhancing an authentication scheme based on conditional privacy-preserving and improving its performance efficiency. This paper reviews the security vulnerabilities of the existing schemes. It also proposes enhancements to the identity-based conditional privacy-preserving authentication scheme to secure and improve the efficiency of VANETs communications. The proposed scheme not only satisfies the security and privacy requirements but also has been proven secure under the random oracle model. Finally, the performance evaluation shows that the proposed scheme is more efficient computationally and communicational than the existing schemes in signing and verifying VANETs messages.

AC-RDV: a novel ant colony system for roadside units deployment in vehicular ad hoc networks

Vehicular ad hoc network (VANET) is a mobile and wireless network that consists of connected vehicles, and stationary nodes called roadside units (RSUs) placed on the aboard of roads to improve traffic safety and to ensure drivers’ and passengers’ comfort. However, deploying RSUs is one of the most important challenges in VANETs due to the involved placement, configuration, and maintenance costs in addition to the network connectivity. This study focuses on the issue of deploying a set of RSUs that is able to maximize network coverage with a reduced cost. In this paper, we propose a new formulation of RSUs deployment issue as a maximum intersection coverage problem through a graph-based modeling. Moreover, we propose a new bio-inspired RSU placement system called Ant colony optimization system for RSU deployment in VANET (AC-RDV). AC-RDV is based on the idea of placing RSUs within the more popular road intersections, which are close to popular places like touristic and commercial areas. Since RSU deployment problem is considered as NP-Hard, AC-RDV inspires by the foraging behavior of real ant colonies to discover the minimum number of RSU intersections that ensures the maximum network connectivity. After a set of simulations and comparisons against traditional RSU placement strategies, the results obtained showed the effectiveness of the proposed AC-RDV in terms of number of RSUs placed, the average area coverage, the average connectivity and the overlapping ratio.

Mobility Prediction issues and Challenges in Vehicular Ad-Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are gaining much attention from both industry and academia communities as an emerging technology. VANET is composed of self-regulating nodes where the vibrant node mobility changes the network topology frequently. The important aim of VANET is to upgrade road safety conditions to minimize extent of road mishaps. The nodes are very mobile in vehicular ad hoc network so routing becomes complex job. As the VANET nodes move very fast the topology changes accordingly. So node mobility prediction in VANET is a very important issue. Further, prediction of mobility helps to estimate the stable path between the nodes which leads to better routing. Estimating secure paths among the routing of nodes perform in a better way, thereby reducing the overhead and minimizing interruptions in connections. This paper explores VANET's basic architecture and discusses a number of current mobility prediction techniques, and concludes with performance analysis of existing routing protocols and proposed mobility prediction methods.

Dynamic multiagent method to avoid duplicated information at intersections in VANETs

Vehicular ad hoc networks (VANETs) allow vehicles to contact one another to provide safety and comfort applications. However, mobility is a great challenge in VANETs. High vehicle speed causes topological changes that result in unstable networks. Therefore, most previous studies focused on using clustering techniques in roads to reduce the effect of vehicle mobility and enhance network stability. Vehicles stop moving at intersections, and their mobility does not impact clustering. However, none of previous studies discussed the impact of vehicle stopping at intersections on base stations (BSs). Vehicles that have stopped moving at intersections continue to send the same information to BSs, which causes duplicated information. Hence, this study proposes a new method named dynamic multiagent (DMA) to filter cluster information and prevent duplicated information from being sent to BSs at intersections. The performance of the proposed method was evaluated through simulations during the use of DMA and without-DMA (W-DMA) methods based on real data collected from 10 intersections in Batu Pahat City, Johor, Malaysia. Overall, the proposed DMA method results in a considerable reduction in duplicated information at intersections, with an average percentage of 81% from the W-DMA method.

B-TSCA: Blockchain Assisted Trustworthiness Scalable Computation for V2I Authentication in VANETs

The rapid development of 5G networks has made smart driving possible. The vehicular ad-hoc networks (VANETs) are the main environment for smart driving, providing road information, instant communication between vehicle and vehicle (V2V) or vehicle and infrastructure (V2I). The information interaction security of VANETs is critical to the proper functioning of the traffic. Much research in recent years has focused on secure communication in VANETs, especially the secure V2V or V2I communications. However, current security schemes often require complex identity re-authentication when vehicles enter a new infrastructure coverage, which greatly reduces the efficiency of the entire network. In addition, the emergence of blockchain has created opportunities to overcome the challenges in VANETs mentioned above. In this article, blockchain is utilized to enhance the scalability of the trustworthiness scalable computation. The proposed blockchain assisted trustworthiness scalable computation based V2I authentication (B-TSCA) scheme achieves rapid re-authentication of vehicles through secure ownership transfer between infrastructures. Note that, trustworthiness scalable computation assisted by blockchian technology ensures the decentralization and non tamperability of the scalable computation result. The security analysis indicates that B-TSCA scheme is a CDH-secure scheme. The time cost of the novel handover authentication phase is half of that of the initial one as is presented in the simulation.

Software-Defined Networking (SDN) based VANET Architecture: Mitigation of Traffic Congestion

In VANETs (Vehicular Ad-hoc Networks), the num-ber of vehicles increased continuously, leading to significant traffic problems like traffic congestion, a feasible path, and associated events like accidents. Though, the Intelligent Transportation System (ITS) providing excellent services, such as safety appli-cations and emergency warnings. However, ITS has limitations regarding traffic management tasks, scalability, and flexibility because of the enormous number of vehicles. Therefore, extending the traditional VANET architecture is indeed a must. Thus, in the recent period, the design of the SD-VANETs (Software-Defined Networking defined VANETs) has gained significant interest and made VANET more intelligent. The SD-VANET architecture can handle the aforesaid VANET challenges. The centralized (logically) SDN architecture is programmable and also has global information about the VANET architecture. Therefore, it can effortlessly handle scalability, traffic management, and traffic congestion issues. The traffic congestion problem leads to longer trip times, decreases the vehicles’ speed, and prolong average end-to-end delay. Though, somewhere, some routes in the network are available with capacity, which can minimize the congestion problem and its characteristics. Therefore, we proposed heuristic algorithms called Congestion-Free Path (CFP) and Optimize CFP (OCFP), in SD-VANET architecture. The proposed algorithms address the traffic congestion issue and also provide a feasible path (less end-to-end delay) for a vehicle in VANET. We used the NS-3 simulator to evaluate the performance of the proposed algorithms, and for generating a real scenario of VANET traffic; we use the SUMO module. The results show that the proposed algorithms decrease road traffic congestion drastically compared to exiting approaches.