Vehicular Ad Hoc Networks Applications Research Articles

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Enhancing Black Hole Attack Detection in VANETs: A Hybrid Approach Integrating DBSCAN Clustering with Decision Trees

Ensuring the security of communication is crucial in Vehicular Ad Hoc Networks (VANETs) to protect the integrity of information sharing among cars. To implement VANET communication as an answer for the different uses, secure communication is necessary. The unreliability of VANET environments is caused by message delays or tampering in VANET applications. Finding the sweet spot between VANET security and performance and dependability is the primary goal. This project’s overarching goal is to fortify VANETs against Blackhole Routing Attacks and, by identifying and blocking harmful nodes, to mitigate the blackhole impact. This paper proposes a robust hybrid approach for the detection of black hole attacks in VANETs, leveraging the synergy between DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Decision Trees. DBSCAN, a density-based clustering algorithm, is employed to identify spatial clusters of vehicles, while Decision Trees are utilized to discern normal communication patterns from malicious ones within these clusters. The integration of these two techniques enhances the accuracy and efficiency of black hole attack detection in the dynamic and resource-constrained VANET environment. Experimental results demonstrate the effectiveness of the proposed hybrid approach, providing a promising solution for bolstering the security of VANETs against emerging threats. Here in result 73.89% improvement is received in Packet Drop Rate using DBSCAN, also minor improvement over Throughput and Average end to end delay and major improvement in terms of Network Routing Load.

Cluster-based multicast optimized routing in VANETs using elite knowledge-based genetic algorithm

The advancement of autonomous and driver-assistance technology has greatly improved the experience of driving. With the growing use of vehicles, there is a greater need for effective communication among them to ensure both applications in Vehicular Ad-hoc Networks (VANETs) for safety and non-safety. VANETs commonly prioritize multicast communication to efficiently use computational resources and achieve a desirable Quality of Service (QoS). However, the dynamic nature of VANETs, marked by high mobility and frequent topology changes, presents significant challenges in establishing and sustaining multicast communication. To ensure that sensing data from all target points is reliably sent to the base station, it is crucial to address the important factors of energy balancing, load balancing, connectivity, and coverage. These factors play a vital role in achieving a balanced and efficient transmission system. In this research paper, clustering-based multicast routing is proposed that utilizes an enhanced Genetic Algorithm Elite Knowledge Sharing Genetic Algorithm (EKSGA) to choose cluster heads. The EKSGA identifies the top 5 % performers with the highest fitness score and generates a new population by exchanging genetic information, aiming to establish a population with the greatest potential for optimal fitness. The performance assessment of the suggested protocol demonstrates significant enhancements in network lifetime and throughput compared to various existing protocols, including LEACH, DEEC, p-WOA, DDEEE, ECBLTR, and GA with improvements ranging from 2.4 % to 22 % in network lifetime and from 3.2 % to 23.2 % in throughput for a system consisting of 500 nodes with energy level set at 0.1 J for rounds 300. Additionally, the proposed approach utilizes the existing population to generate a more fit population, thereby reducing the need for excessive iterations and computational overhead.

Transforming Vehicular Networks: How 6G can Revolutionize Intelligent Transportation?

Vehicular Ad-hoc Networks (VANETs) have enabled intelligent transportation systems by facilitating communication between vehicles and roadside infrastructure. However, the current 5G and 4G networks that support VANETs have certain limitations that hinder the full potential of VANET applications. These limitations include constraints in bandwidth, latency, connectivity, and security. The upcoming 6G network is expected to revolutionize VANETs by introducing several advancements. 6G will provide ultra-fast communication with significantly reduced latency, enabling real-time and high-bandwidth data exchange between vehicles. The network will also offer highly reliable and secure connectivity, ensuring the integrity and privacy of VANET communications. Precise localization and sensing capabilities will be enhanced in 6G-based VANETs, enabling accurate positioning of vehicles and improved situational awareness. This will facilitate collision avoidance, traffic management, and cooperative driving applications. Moreover, integrating edge computing in 6G networks will bring computing resources closer to the edge, lowering response times and facilitating faster decision-making in time-critical scenarios. This paper explores the key features and capabilities of 6G technology and how it can revolutionize intelligent transportation, addressing challenges and opportunities for adopting 6G in VANETs.

Fog Computing in Vehicular Ad Hoc Network Applications: A Survey of Challenges and Scope

Abstract: The use of computationally intensive applications requiring substantial storage resources in vehicular ad hoc networks is on the rise due to the technology shift from smart, intelligent vehicles to autonomous vehicles. Cloud computing addresses this problem to some extent by providing computational and storage facilities for the significant amount of data generated by vehicular nodes. However, it still cannot meet the stringent requirements of highly dynamic vehicular nodes in a real-time environment. The delay-sensitive applications of vehicular ad hoc networks instill the need for computing facilities in the closest possible proximity to the vehicular nodes. Therefore, fog computing has found potential in vehicular ad hoc network applications. Fog computing brings storage and computing resources closer to vehicles. Fog-based vehicular ad hoc networks can help improve the issues of first-generation vehicular ad hoc networks, such as latency, location perception, and concurrent response. Based on existing research, using fog computing decreased the delay by 50-60% and 60-70% in vehicular ad hoc network safety and entertainment and commercial applications, respectively, compared to cloud computing. An extensive study of the amalgamation of fog computing and vehicular ad hoc networks should be conducted to understand the underlying challenges of fog-based vehicular ad hoc networks. Existing surveys only include specific applications of this technology. However, we present an extensive survey focusing on the architecture of these two technologies, their integration, use cases of fog vehicular ad hoc networks, simulation tools, important performance metrics, the challenges in multiple contexts, and the gaps that need to be addressed, as well as suggest prominent journals in this field. This paper will not only impart knowledge to researchers in this area but also provide them with insights to identify the challenges and pursue their work further in fog-based vehicular ad hoc networks.

Ensuring Security and Privacy in VANET: A Comprehensive Survey of Authentication Approaches

Vehicular ad hoc networks (VANET) are revolutionizing intelligent transportation systems (ITS), and as a result, research on their security is becoming increasingly important. As the primary security concern for VANET, authentication security is still quite difficult to achieve. Consequently, the prior knowledge of VANET is covered in this survey before outlining the primary security concerns. To set itself apart from previous surveys, this study suggests security properties and challenges among VANET. Next, the essential and significant features of a secure VANET system, such as confidentiality and integrity of data, and the availability of network systems have been reported, the authenticity of nodes and messages, and the refusal to deny data once it has been transmitted is detailed. Later, it outlined the requirement of the ITS which makes the survey unique. More importantly, the report on the most recent developments in VANET concentrates on the authentication schemes that have been proposed recently. The security features and authentication resistance against attacks, along with the overhead and efficiency of these schemes, are thoroughly examined and contrasted. A detailed analysis of V2V, V2I, and V2X authentication is been reported. Various cryptographic schemes have been discussed along with some advanced techniques such as Blockchain and hybrid schemes. An overview of the integration of 5G/6G networks is documented. Applications of VANET have been discussed in detail along with some open challenges in VANET. In summary, this work reviews a few lessons learned and explores different possibilities for future research.

Network data Security based on Routing Algorithm: Application in Vehicular ad-hoc Networks

Network data Security based on Routing Algorithm: Application in Vehicular ad-hoc Networks

A Security-Enhanced Certificateless Conditional Privacy-Preserving Authentication Scheme for Vehicular Ad Hoc Networks

By adopting advanced Internet of Things (IoT) technology to sense and collect traffic-related information to improve traffic safety and efficiency, the vehicular ad hoc network (VANET) is becoming a prominent application that changes human driving experiences in the current era. Because frequent data exchange occurs in open environments, VANETs are inherently vulnerable to security and privacy attacks. In history, many certificateless aggregate signature (CLAS) schemes with conditional privacy-preserving (CPP) have been proposed to ensure the authenticity and integrity of the exchanged data and protect users’ privacy. However, we reveal that the state-of-the-art schemes cannot be deployed in practical VANET applications by proposing concrete signature forgery attacks. To this end, we propose a new CLAS-based authentication scheme with CPP for VANETs. The rigorous security proofs based on the standard cryptographic assumption show that the scheme has enhanced security. Moreover, theoretical analysis and experimental evaluation illustrate the practicality of our design.

Decentralized Receiver-based Link Stability-aware Forwarding Scheme for NDN-based VANETs

The inter-vehicle communication in the vehicular ad-hoc network (VANET) providing ubiquitous mobility to mobile users is considered an essential component of intelligent transportation systems (ITS), enabling a wide range of safety and non-safety applications in which the vehicles seamlessly interact and co-operate with each other. The recent paradigm shift in VANETs protocols has led to the adoption of name data networking (NDN) as an underlying communication protocol in VANETs that replaces traditional address-based communication philosophy, treats content as a first-class citizen, and assigns unique names to contents consequently enabling name-based routing and forwarding, caching, and security. Although NDN has significantly improved the underlying communication issues in VANETs, a plethora of challenges such as message broadcast storms, in-efficient packet suppression mechanisms, intermittent connectivity owing to frequent topological changes, and reverse path partioning (RPP) require further attention from the research community. To cope with these issues, this paper proposes decentralized receiver-based link stability-aware forwarding (DRLSF) protocol. The DRLSF is a beacon-less receiver-based multi-hop protocol, well-suited for pull-based applications where end users request desired data by sending packets. The comparative performance analysis validates the effectiveness of the DRLSF protocol in different VANET environments and application scenarios.

Challenges and Solutions for Vehicular Ad-Hoc Networks Based on Lightweight Blockchains

Current research with Vehicular Ad-hoc Networks (VANETs) has focused on adapting an efficient consensus mechanism and reducing the blockchain size while maintaining security. Care must be taken when implementing blockchains within VANET applications to leverage the chains’ strengths while mitigating their weaknesses. These chains can serve as distributed ledgers that provide storage for more than financial transactions. The security provided by longer blockchains constitutes a nearly immutable, decentralized data structure that can store any data relevant to the applications. However, these chains must be adapted to the ad-hoc, resource-constrained environments found in VANETs. In the absence of abundant resources and reliable network connections, chain operation and maintenance must address the challenges presented by highly mobile nodes in novel ways, including situations such as emergency messaging that require real-time responses. Researchers have included different mechanisms to realize lightweight blockchains, such as adding reputation to existing consensus mechanisms, condensing the consensus committees, using geographical information, and monitoring a nodes behavior in attempts to adapt blockchains to these domains. This paper analyzes the challenges and gives solutions for these different mechanisms to realize lightweight blockchains for VANETs.

Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning

A significant subset of ubiquitous computing known as vehicular ad hoc networks (VANETs) serves as a foundational piece of technology for VANET applications. Blockchain technology can be used in microgrids to conduct safe point-to-point transactions between anonymous parties while also addressing needs for security. This research is o proposes novel method in IoT(internet of things) based edge cloud computing architecture with microgrid energy management of VANET. here the VANET communication is carried out based on IoT edge cloud computing module and the smart microgrid architecture is used for energy management in VANET. then each vehicle energy has been analysed using structural reinforcement variational encoder neural networks. experimental analysis is carried out in terms of energy efficiency, network lifetime, training accuracy, QoS, communication overhead. the proposed technique attained energy efficiency of 96%, network lifetime of 85%, training accuracy of 98%, QoS of 95%, communication overhead of 55%.

A comprehensive and configurable simulation environment for supporting vehicular named-data networking applications

The Named Data Networking (NDN) architecture, with its network-layer features, services, and properties, is well-suited for vehicular applications and inter-vehicle communication (IVC). In contrast, IP-based host-centric architectures struggle with challenges inherent to the vehicular ad-hoc network (VANET) context, such as node mobility, data security, efficient data forwarding, and routing. Named Data Networking takes a fundamental departure from today’s IP-based architectures for VANET/IVC and thus requires extensive experimentation and evaluation. To facilitate experimentation with Vehicular Named-Data Networking, we present the NDN4IVC, an open-source simulator/framework that facilitates testing of more realistic VANET applications via the NDN stack. This project utilizes two popular simulators for VANET simulation: the NS3 network simulator with the ndnSIM module and SUMO, a simulator of urban mobility. NDN4IVC allows real-time bidirectional communication between SUMO and NS3 to support more data about road traffic and vehicular mobility. The framework can model the impact of vehicular networks on road traffic and investigate complex interactions between the two domains. We included two sample applications in VANET to demonstrate how different NDN properties can be used. Experiments were conducted as proof-of-concept studies to demonstrate the potential of the framework and its functionality.

Blockchain-Assisted Privacy-Preserving and Context-Aware Trust Management Framework for Secure Communications in VANETs.

Vehicular ad hoc networks (VANETs) are used for improving traffic efficiency and road safety. However, VANETs are vulnerable to various attacks from malicious vehicles. Malicious vehicles can disrupt the normal operation of VANET applications by broadcasting bogus event messages that may cause accidents, threatening people's lives. Therefore, the receiver node needs to evaluate the authenticity and trustworthiness of the sender vehicles and their messages before acting. Although several solutions for trust management in VANETs have been proposed to address these issues of malicious vehicles, existing trust management schemes have two main issues. Firstly, these schemes have no authentication components and assume the nodes are authenticated before communicating. Consequently, these schemes do not meet VANET security and privacy requirements. Secondly, existing trust management schemes are not designed to operate in various contexts of VANETs that occur frequently due to sudden variations in the network dynamics, making existing solutions impractical for VANETs. In this paper, we present a novel blockchain-assisted privacy-preserving and context-aware trust management framework that combines a blockchain-assisted privacy-preserving authentication scheme and a context-aware trust management scheme for securing communications in VANETs. The authentication scheme is proposed to enable anonymous and mutual authentication of vehicular nodes and their messages and meet VANET efficiency, security, and privacy requirements. The context-aware trust management scheme is proposed to evaluate the trustworthiness of the sender vehicles and their messages, and successfully detect malicious vehicles and their false/bogus messages and eliminate them from the network, thereby ensuring safe, secure, and efficient communications in VANETs. In contrast to existing trust schemes, the proposed framework can operate and adapt to various contexts/scenarios in VANETs while meeting all VANET security and privacy requirements. According to efficiency analysis and simulation results, the proposed framework outperforms the baseline schemes and demonstrates to be secure, effective, and robust for enhancing vehicular communication security.

Implementation of DNN-Based Physical-Layer Network Coding

Physical-layer network coding (PNC) has been proposed to double the throughput of a wireless two-way relay network (TWRN) and has been adapted so that it can be applied in vehicular ad-hoc networks (VANETs). Deep neural network (DNN)-based physical-layer network coding (PNC) for the TWRN system has been studied in recent years, under the assumption that the channel state information (CSI) is available to the end nodes and the relay node. However, the state-of-the-art DNN-based PNC system was only feasible for theoretical simulations, and no practical system design has been studied. When moving from theory to practical wireless systems, there are two critical issues to tackle: 1) unlike conventional regularly shaped quadrature amplitude modulations (QAMs), the constellation points of a DNN-based PNC system's end nodes are irregular and unpredictable to the relay node; and 2) other than in high-precision simulations, the practical DNN-based PNC system's constellation points are not fixed, that is, there is usually a cluster of points that must be grouped together so that the transceiver's digital-to-analog converter (DAC) and analog-to-digital converter (ADC) can work properly. To solve the above issues, we proposed the DNN-PNC implementation that can be realized using the Universal Software Radio Peripheral (USRP) platform. The computational complexity of our simulated DNN-PNC is one order of magnitude smaller than that given in the literature, while maintaining a good bit error rate (BER). In the implementation phase, we find that the naturally-happened power imbalance between the two end nodes' signals in our implementation can significantly boost the PNC constellation recovery, and hence reduce the system BER. Our experimental results showed that the BER of DNN-PNC can be lower than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-4}$</tex-math></inline-formula> when the signal-to-noise ratio (SNR) is 16 dB for the 2-QAM case and 20 dB for the 4-QAM case, respectively. These results indicate that DNN-based PNC is feasible for VANET applications, where the CSI changes and is difficult to obtain.

Low calculation cost of HEVC coding unit size based on spatial homogeneity detection

For the same video quality, HEVC gives 25% to 50% bitrate savings, compared to its predecessor the Advanced Video Coding H.264 and thus supports resolutions up to 8 K UHD. However, the reduction in bitrates provided by the HEVC leads to an increase in the computational cost of the encoding operation. This complexity can become a true handicap especially for real-time video streaming and also for VANET (Vehicular Ad-Hoc Network) applications such as traffic safety and Video surveillance. The improvement in the bitrates and also the increase in the calculation cost are due to the use of large and multi-sized coding, prediction and transform blocks. Indeed, the H264 coder is based on structure macroblocks with sizes 4 × 4, 8 × 8 and 16 × 16, while H.265 depends on Coding Tree Units (CTUs), CTUs select sizes 4 × 4, 8 × 8, 16 × 16, 32 × 32 and 64 × 64 blocks. This paper proposes a fast CU (Coding Unit) size decision method to reduce the HEVC calculation cost based on spatial homogeneity. Compared with the HM16.13 benchmark test model, the average coding time is reduced by around 40% for CIF / QCIF video sequences, 35% to 43% for class A, B and C test sequences. These important reductions in coding time are obtained with negligible loss of quality and an average increase in bitrates which does not exceed 0.89% for the three configuration modes (All intra, Random Access and Low Delay).

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.

A novel and lightweight wireless communication scheme for Vehicular Ad hoc Networks

Vehicular Ad hoc Network (VANET) has attracted more and more attention from academia and industry because of its self-organization, dynamics, and scalability. However, since the overwhelming majority of communication in VANET adopt wireless communication, which is vulnerable to attack, coupled with the high requirements of communication efficiency for dynamic VANET, the wide application of VANET still faces great challenges. In this paper, we propose a lightweight communication scheme (LWCS) suitable for VANET. The proposed scheme includes a lightweight authentication scheme and a one-to-many wireless communication scheme. The authentication model is based on ECDLP and message authentication code (MAC), which realizes mutual authentication and improves efficiency. The one-to-many communication model is different from the existing scheme using bilinear pairing. Instead, we take advantage of Chinese remainder theorem (CRT), so that the receiver can quickly reconstruct the key. Besides, the length of the ciphertext is not affected by the number of receivers. Security analysis shows that our scheme is secure under the assumptions that the IFP and ECDLP are intractable. Meanwhile, the proposed scheme satisfies multiple security requirements such as forward security, backward security and common attacks resistance. The performance analysis demonstrates theoretically and experimentally that our scheme is efficient compared with related schemes.

Lightweight and Secure Multi-Factor Authentication Scheme in VANETs

The idea of a smart city and the utilization of vehicles are emerging rapidly. For inter-vehicular communication, this progression builds Vehicular Ad-Hoc Network (VANET) an extensively used network. The VANET is used to obtain information regarding the road's condition, location of vehicles, speed, and traffic congestion description. The exchange of information in VANET is public which leads to severe security threats. Generally, one of the most crucial tasks in VANET is the security of data. So it becomes one of the urgent tasks of researchers. We have reviewed several authentication schemes for VANETs. Still, most schemes are not secure due to several attacks such as replay, denial of services, impersonation attacks, etc. Furthermore, the existing schemes used higher communication and computational costs, and they are also incompatible with VANET applications that are resource-constrained. This article presents the lightweight and secure multi-factor authenticated key agreement scheme for VANETs to ensure efficient and secure transmission of data via an open channel. Our presented scheme is secure, scalable, and lightweight compared to existing schemes due to its low computation and communication cost. The formal and informal security analysis proved that our scheme is more reliable, practical, and secure against several unknown adversarial attacks in VANETs.

ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.