Performance Of Vehicular Ad Hoc Networks Research Articles

Secure Vehicle-to-Vehicle Communication Using Routing Protocol Based On Trust Authentication Secure Sugeno Fuzzy Inference System Scheme

Introduction: Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays. Method: To overcome the drawbacks, this work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks. objective: To overcome the drawbacks, this work proposed a new method based on Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the security performance of VANET. Result: In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission. Conclusion: Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

EAPRAD: A MAC protocol for Enhancing Access Probability and Reducing Access Delay in VANETs

Vehicular Ad Hoc Networks (VANETs) present challenges in terms of access collision and merge collision, leading to increased access delay in end-to-end vehicle communication. How to address these issues remains an unsolved problem. In this paper, we propose EAPRAD (Enhancing Access Probability and Reducing Access Delay), a novel MAC (Media Access Control) protocol based on a Markov model. Our protocol defines the number of vehicle accessing time slot failures, with different failure states forming a partial Markov chain. The highest failure status vehicle is prioritized to access the time slot, effectively resolving the problem of indefinite time slot reservations during the back-off phase. Furthermore, after acquiring the time slot, vehicles may still experience package collision, particularly when vehicles traveling in opposite directions share the same frequency band. To address this, our protocol introduces a new MAC frame structure that partitions available frequency band resources based on distinct driving directions, eliminating merge collision in opposite directions. For vehicles traveling in the same direction, intermediate nodes are utilized to proactively anticipate and prevent package collision. To model the state transition of access collision probability and merge, we develop a Continuous Time Markov Chain (CTMC) analytical model. Simulation results demonstrate that EAPRAD reduces end-to-end delay and beacon lost rate (BLR), thereby improving overall network performance in VANETs.

A clustering approach to improve VANETs performance

Vehicular ad-hoc network (VANET) is a technique that uses cars moved in cities or highways as nodes in wireless networks. Each car in these networks works as a router and allows cars in the range to communicate with each other. As a result of this movement, some cars will become out of range, but these networks can connect to the internet and the cars in these networks can connect to each other. This research proposes a unique clustering strategy to improve the performance of these networks by making their clusters more stable. One of the biggest problems these networks face is traffic data, which consumes network resources. Agent based modeling (ABM) evaluates better networks. The evaluation showed that the proposed strategy surpasses earlier techniques in reachability and throughput, but ad hoc on-demand distance vector (AODV) (on-demand/reactive) outperforms it in total traffic received since our hybrid approach needs more traffic than AODV.

Maximizing VANET performance in cluster head selection using Intelligent Fuzzy Bald Eagle optimization

Vehicular Ad-hoc Network (VANET) is a type of wireless network that allows communication among vehicles and roadside units to develop advanced intelligent transportation systems. The success of VANETs depends on the stability of wireless communication, among vehicles, which is challenging to achieve due to high vehicle speed, rapidly changing topology, and unstable communication links. Moreover, the instability of the network caused by the mobile nature of vehicles in VANET reduces the performance of the network. Clustering in VANETs is a crucial technique that organizes the network and forms the basis of the routing protocol. Clustering algorithms are designed for VANETs to work efficiently and require several phases that must be integrated into the process before a clustering decision can be made. Therefore, this paper presents a novel Intelligent Fuzzy Bald Eagle (IFBE) optimization to enhance the performance of VANETs by optimizing the cluster head selection (CHS) process. The experimental results prove that the IFBE approach outperforms other existing mechanisms in terms of energy consumption, end-to-end delay, and packet delivery ratio. The proposed mechanism utilizes an intelligent fuzzy system to optimize the CHS process. The fuzzy system uses a set of rules and membership functions to evaluate the candidate nodes' suitability for being cluster heads. The Bald Eagle Search (BES) optimization meta-heuristic algorithm is used to find the optimal values for the fuzzy system's membership functions. The proposed mechanism was evaluated using the MATLAB simulator. Finally, the experimental result proved that the IFBE approach achieved minimum delay and energy consumption of 13.58 ms, and 15.5 J, and higher clustering efficiency and packet delivery rate of 94.15% and 97.65% respectively, which show that it performs better than other existing approaches.

A Multi-Objective Roadside Unit Deployment Model for an Urban Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical.

FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs

Vehicular ad hoc networks (VANETs) are used for vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. They are a special type of mobile ad hoc networks (MANETs) that can share useful information to improve road traffic and safety. In VANETs, vehicles are interconnected through a wireless medium, making the network susceptible to various attacks, such as Denial of Service (DoS), Distributed Denial of Service (DDoS), or even black hole attacks that exploit the wireless medium to disrupt the network. These attacks degrade the network performance of VANETs and prevent legitimate users from accessing resources. VANETs face unique challenges due to the fast mobility of vehicles and dynamic changes in network topology. The high-speed movement of vehicles results in frequent alterations in the network structure, posing difficulties in establishing and maintaining stable communication. Moreover, the dynamic nature of VANETs, with vehicles joining and leaving the network regularly, adds complexity to implementing effective security measures. These inherent constraints necessitate the development of robust and efficient solutions tailored to VANETs, ensuring secure and reliable communication in dynamic and rapidly evolving environments. Therefore, securing communication in VANETs is a crucial requirement. Traditional security countermeasures are not pertinent to autonomous vehicles. However, many machine learning (ML) technologies are being utilized to classify malicious packet information and a variety of solutions have been suggested to improve security in VANETs. In this paper, we propose an enhanced intrusion detection framework for VANETs that leverages mutual information to select the most relevant features for building an effective model and synthetic minority oversampling (SMOTE) to deal with the class imbalance problem. Random Forest (RF) is applied as our classifier, and the proposed method is compared with different ML techniques such as logistic regression (LR), K-Nearest Neighbor (KNN), decision tree (DT), and Support Vector Machine (SVM). The model is tested on three datasets, namely ToN-IoT, NSL-KDD, and CICIDS2017, addressing challenges such as missing values, unbalanced data, and categorical values. Our model demonstrated great performance in comparison to other models. It achieved high accuracy, precision, recall, and f1 score, with a 100% accuracy rate on the ToN-IoT dataset and 99.9% on both NSL-KDD and CICIDS2017 datasets. Furthermore, the ROC curve analysis demonstrated our model’s exceptional performance, achieving a 100% AUC score.

Path Loss and Auxiliary Communication Analysis of VANET in Tunnel Environments

Vehicular ad hoc network (VANET) communications face severe fading problems due to the signal reflections and diffractions within tunnels. Unlike the open road, the space of a tunnel is very limited, so VANET communication performance in a tunnel is seriously affected. In the process of signal transmission, the reflected signal is symmetrical with the incident signal after it is reflected by the road and the wall. In this paper, we establish a mathematical model of path loss for V2V (Vehicle-to-Vehicle) communication based on the principle of signal reflection symmetry in tunnels and considering several factors, such as the tunnel surface and the color of the tunnel wall. In addition, we use cooperative communication to form a virtual multiple-input multiple-output (V-MIMO) system, to improve the communication quality in tunnels. In the proposed system, the OBU (On-Board-unit) and RSU (Road-Side-Unit) share each other’s antennas, so that wireless cooperative communication can be employed, without increasing the number of antennas in a one-way tunnel. Therefore, this multipath fading internal electromagnetic wave propagation model can be used to improve performance. A deep reinforcement learning algorithm was used to solve the pairing problem to obtain a more accurate OBU and RSU pair, to form a V-MIMO system. Here, the RSU is regarded as an agent and interacts with the OBU in the tunnel. The optimal strategy was learned in a real-time changing simulation environment, and the experiment verified the convergence of the algorithm. The simulation results showed that, compared with the Q-learning based scheme, the optimal matching algorithm based on V-MIMO and a DQN (Deep Q-network) could effectively reduce the probability of transmission outages and improve the communication efficiency in tunnels.

A Novel Identity-Based Privacy-Preserving Anonymous Authentication Scheme for Vehicle-to-Vehicle Communication

This paper proposes a novel bilinear pairing-free identity-based privacy-preserving anonymous authentication scheme for vehicle-to-vehicle (V2V) communication, called “NIBPA”. Today, vehicular ad hoc networks (VANETs) offer important solutions for traffic safety and efficiency. However, VANETs are vulnerable to cyberattacks due to their use of wireless communication. Therefore, authentication schemes are used to solve security and privacy issues in VANETs. The NIBPA satisfies the security and privacy requirements and is robust to cyberattacks. It is also a pairing-free elliptic curve cryptography (ECC)-based lightweight authentication scheme. The bilinear pairing operation and the map-to-point hash function in cryptography have not been used because of their high computational costs. Moreover, it provides batch message verification to improve VANETs performance. The NIBPA is compared to existing schemes in terms of computational cost and communication cost. It is also a test for security in the random oracle model (ROM). As a result of security and performance analysis, NIBPA gives better results compared to existing schemes.

Measurement-Based Analysis on Vehicle-to-Vehicle Connectivity in Tunnel Environment

Vehicular ad hoc network (VANET) brings an excellent solution to ensure road safety and transportation efficiency in critical environment like tunnel. Particularly, radio link connectivity of vehicle-to-vehicle (V2V) significantly influences the performance of VANETs. The communication range of the radio systems is a random variable in reality due to the channel fading effect. Therefore, the connectivity model between vehicles in realistic environment is a key for accurate evaluation of system performances. In this paper, we study the V2V connectivity performance in the presence of channel randomness for tunnel environment. Firstly, based on channel measurement campaign, empirical path loss (PL) and small-scale fading channel models are established. Secondly, we study the influence of large-scale fading parameters on V2V connectivity. Thirdly, based on real small-scale fading characteristics, we derive the V2V connectivity probability between any two vehicles under Nakagami fading channel for one-dimensional VANET, and give the closed-form of V2V connectivity probability. Finally, we study the influences of various parameters (i.e., Nakagami fading factor, vehicle density, and neighbor order) on V2V connectivity performance. Results show that with the Nakagami fading shape factor increases, the connectivity probability increases. The shadowing fading can improve connectivity in the VANET; the path loss exponent, transmission distance, and signal-to-noise ratio (SNR) threshold have a negative impact on connectivity probability. The transmit power, vehicle density, and path loss threshold value have a positive impact on connectivity.

Connectivity Enhancement of E-VANET Based on QL-mRSU Self-Learning Energy-Saving Algorithm

With the development of smart cities and smart electric vehicles (EVs), the problem of improving the performance of Vehicular Ad-hoc Networks (VANETs) is gradually being emphasized. To improve the network performance of VANETs, some scholars have considered parked vehicles as roadside units, but have not paid attention to the energy consumption characteristics of vehicles, especially electric vehicles. Therefore, in this paper, we propose a QL-mRSU series artificial intelligence energy saving method to optimize the energy consumption of parked electric vehicles during communication. The method is based on electric vehicle self-organizing networks (E-VANETs), which dynamically cluster electric vehicles parked in parking lots by parameters such as traffic flow, number of service demands, and charging index in reinforcement learning, select the most suitable vehicles as mobile roadside units (mRSUs), and adjust the working mode according to environmental changes such as the number of service demands to achieve the effects of self-learning and energy saving. The simulation experimental results show that compared with other energy-based routing algorithms, the method is able to make optimal choices through self-learning with guaranteed communication quality and is more adaptable to traffic flow changes on the road, thus ensuring the stability of energy-saving efficiency. In addition, the method significantly improves the energy structure of electric vehicle parking clusters.

Improving AODV Performance by Software Defined Networking Using NS3

Abstract Nowadays, vehicular networks attract car manufacturers, network researchers, and governments as well. They represent one of the building blocks, for the intelligent transportation systems. Our task is to study the employment of SDN advantages to facilitate and improve the performance of vehicular ad-hoc networks. The goal of the research is to evaluate AODV routing protocol performance improved with SDN technology applied on VANET network in specified environment of a city. We have evaluated three parameters: packet delivery ratio, end-to-end delay and throughput using SUMO and NS3 simulators. The implemented evaluation protocol shows the importance of the adopted approach.

A Novel Fuzzy Logic-Based Scheme for Malicious Node Eviction in a Vehicular Ad Hoc Network

Securing communication in vehicular ad hoc networks (VANETs) is hampered by numerous constraints, making it more difficult. First, traditional security schemes cannot be directly applied in VANET because they consider fixed topology. Second, VANET enables dynamic spectrum access where nodes constantly change frequencies due to their high degree of mobility, resulting in severe consequences on network performance. Third, an effective security scheme in VANET needs local and continual knowledge of nodes. Last, the presence of malicious nodes and their misbehaving activities impair the safety of the drivers since they might alter the content of the sent safety alerts. With these constraints in mind, this paper presents a unique security strategy that utilizes node behaviour during message exchange as a security metric to address these issues. Through the message alert exchange phase, node behaviour is measured through the fuzzy logic framework to generate a rank for each node called trust level (BL), which describes the node’s reliability in exchanging safety messages correctly. Moreover, all messages in VANET are encrypted using the existing cryptography techniques. The proposed scheme is developed to enhance communication security in VANET, minimize the effects of malicious nodes, and improve resource utilization in VANET. Evaluation of the proposed scheme shows that it improves the performance of VANET in terms of end-to-end delay, packet delivery ratio, and packet loss ratio. According to the results, our scheme improves throughput by up to 23% and reduces end-to-end delay by up to 60%.

Adaptive and Optimized Control Channel Utilization in Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) are counted as a state-of-art technology which can be considered to provide reliable communication which is increase the safety and convenience of drivers in the road. Due to channel congestion occurrence in both highway and urban scenario, many VANET’s safety applications face degradation in their quality of service especially in a high-density environment. To improve the performance, reliability, and safety of VANETs, message overhead on the standard wireless channel dedicated short-range communications (DSRC) should be alleviated to guarantee safety-related applications. To this end, this paper proposes an approach to optimize the utilization of the control channel (CCH) and make the control channel interval (CCHI) adaptive to the emergency cases in the vehicular scenarios. More precisely, when there is an emergency cases, a part of service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery. When a vehicle intended to transmit the safety message to the surround vehicles, the proposed method granted that CCH is allocated to meet the traffic safety demands; then, the safety messages disseminate properly to the transmission range. A part of the service channel interval (SCHI) is exploited to ensure the guarantee of emergency message delivery, Then, applying the laying chicken algorithm (LCA) to solve the derived CCH optimized utilization. Finally, extensive simulations are carried out to validate the proposed optimized CCH utilization. To validate the performance of the proposed adaptive congestion control approach (ACCA), we used the network simulator Ns-3 [35] to simulate the vehicle’s environment, after generating vehicles’ mobility traces of the nodes using the network simulator for urban mobility (SUMO) with OpenStreetMap (OSM). The simulation indicates that our congestion algorithm (ACCA) performs better service quality than other existing algorithms. We got a better network connection regarding safety messages, beacon rate with time, and packet length for different vehicles’ densities.

Reliability Evaluation of IEEE 802.11p Broadcast Ad Hoc Networks on the Highway

In a vehicular ad hoc network (VANET) based on IEEE 802.11p, the performance of the communication link is heavily influenced by interference. To quantify the impact of interference, analytical models usually explicitly or implicitly assumed the interference range beyond which interference is ignored. We find that the potential maximum interference range should be related to the minimum interference power that the device can perceive. However, most previous works on VANET performance modeling simply assumed a fixed interference range. The most recently proposed effective interference range dependent on the signal-to-interference-and-noise ratio (SINR) threshold may appear to exceed the potential maximum interference range, resulting in an overestimation of the impact of interference. This paper proposes the SINR related Effective Distance Constrained by the Maximum interference range-based (SED-CM) model for IEEE 802.11p VANET performance evaluation on the Highway. First, we give the potential maximum interference range setting by studying the empirical distribution of interference distance corresponding to the minimum interference power in NS2 simulation. The performance metrics are then derived. The proposed SED-CM model is further extended to the intersection scenario. We conducted extensive NS2 simulations to evaluate the proposed model and compare it with three typical models. Results show that the SED-CM model presents the best evaluation ability. Moreover, we experimentally analyzed the effect of vehicle speed on the performance of VANET by mapping the vehicle speed to the vehicle density. Results show that a faster beacon rate is required to meet the reliability requirement of safety applications with the increase in vehicle speed.

G-3MRP: A game-theoretical multimedia multimetric map-aware routing protocol for vehicular ad hoc networks

The particular requirements and special features of vehicular ad hoc networks (VANETs) (e.g., special mobility patterns, short link lifetimes, rapid topology changes) involve challenges for the research community. One of these challenges is the development of new routing protocols specially designed for VANETs. In this paper, we present a novel game-theoretical approach of a multimetric geographical routing protocol for VANETs to forward video-reporting messages in smart cities. Game theory is considered a very interesting theoretical framework to analyze and optimize resource allocation problems in digital communication scenarios. Our contribution has shown to enhance the overall performance of VANETs in urban scenarios, in terms of percentage of packet losses, average end-to-end packet delay and peak signal to noise ratio (PSNR).

A Systematic Review of Swarm Intelligence Algorithms to Perform Routing for VANETs Communication

The performance of Vehicular Adhoc Networks (VANETs), the underlying technology for intelligent vehicles, has shown improvement with the application of swarm-based algorithms for routing. Swarm Intelligence is a self-intelligence group of similar agents functioning on distribution, flexibility, and communication among the agents. The multiple problems occurring in the modern communication systems, including VANETs, have been tried to be resolved by the application of swarm intelligence algorithms viz, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuck-oo Search Algorithm (CSA), Glowworm Swarm Optimization (GSO), etc., have been proposed in the literature. This paper provides a comparative analysis of operations of swarm-based algorithms. Points like their operations, basic entities, advantages, disadvantages, and applications are discussed.

Distributed Channel and Power Level Selection in VANET Based on SINR using Game Model

This paper proposes a scheme of channel selection and transmission power adjustment in Vehicular Ad hoc Network (VANET) using game theoretic approach. The paradigm of VANET enables groups of vehicles to establish a mesh-like communication network. However, the mobility of vehicle, highly dynamic network environment, and the shared-spectrum concept used in VANET pose some challenges such as interference that can decrease the quality of signal. Channel selection and transmit power adjustment are aimed to obtain the higher signal to interference and noise ratio (SINR). In this paper, game theory is implemented to model the channel and power level selection in VANET. Each vehicle represents the player and the combination of channel and power level represents the strategy used by the player to obtain the utility i.e. the SINR. Strategy selection is arranged distributively to each player using Regret Matching Learning (RML) algorithm. Each vehicle evaluates current utility obtained by selecting a strategy to define the probability of that strategy to be selected in the next time. However, RML has a shortcoming for using assumption that hard to be implemented in real VANET environment. Therefore modification of RML devised for this application is also proposed. The simulation model of channel and power level selection is build to evaluate the performance of the proposed scheme. The results of simulation display the improvement of VANET performance in term of SINR and throughput from the proposed scheme.

Priority-Based Hybrid MAC Protocol for VANET with UAV-Enabled Roadside Units

Vehicular ad hoc networks (VANETs) connect vehicles with other network units through wireless communication, thus improving traffic efficiency and safety by interacting with traffic information in networks. Roadside unit (RSU) plays an important role in VANETs in connecting vehicles with the transportation centre. With the rapid development of unmanned aerial vehicles (UAV), UAV-enabled RSU (U-RSU) is an effective way to promote the performance of VANETs. In this paper, a novel medium access control (MAC) layer protocol named PHB-MAC is proposed based on the priority setting of transmitted messages. Firstly, the priority-based contention mechanism is presented to ensure the transmission efficiency of the safety message. Then, the proposed MAC protocol combining contention and adaptive scheduled scheme is described in detail. Finally, the numerical analysis and simulation results of PHB-MAC and other existing protocols are presented. It is revealed that the proposed protocol improves throughput by at least 30%. Meanwhile, it reduces packets drop rate and delay by 21% and 22%, respectively. Finally, the numerical analysis and simulation results are given to prove the advancement of the proposed protocol on throughput, packets drop rate (PDR), and delay.

A Survey of VANET/V2X Routing From the Perspective of Non-Learning- and Learning-Based Approaches

Intelligent transportation systems (ITSs) have become increasingly popular because they support effective coordination in connected vehicles. ITSs present an integrated approach for exchanging relevant information in order to improve the safety, efficiency, and reliability of road transportation systems. A variant of mobile ad-hoc networks (MANETs) called vehicular ad-hoc networks (VANETs) are an integral component of ITSs. VANETs consist of interconnected vehicles with sensing abilities that exchange information related to traffic, positioning, weather, and emergency services. In general, vehicle-to-everything (V2X) refers to communications between any entity and a vehicle, where the entity may be a vehicle, a cloud-based network, a pedestrian, or equipment installed along a road. One of the crucial challenges in V2X is the reliable and timely circulation of information among vehicular nodes to allow drivers to make decisions that increase road safety. In this context, efficient V2X routing protocols play a key role in supporting reliability and safety, and enhancing the overall quality of service (QoS) in VANETs. However, VANETs have distinct characteristics, such as high vehicular node mobility, unsteady connectivity, rapid changes in network topology, and unbounded network size, that can significantly affect routing in the network. Various routing protocols for V2X communication exist in the open technical literature. In this survey, we categorize the routing mechanisms as non-learning- and learning-based approaches, and discuss existing V2X routing protocols and their contributions to and impacts on VANET performance. Here, the learning-based approach implies the use of machine learning algorithms. This survey also summarizes open challenges in designing effective V2X routing protocols and future research directions to consider when developing smart routing mechanisms for next-generation intelligent VANET technologies.

Impact of Block Data Components on the Performance of Blockchain-Based VANET Implemented on Hyperledger Fabric

Blockchain, a vital technology in today’s era, changed the way we share, manage and exchange our data in a centralized way to decentralized architecture. With the increasing demand for Blockchain, various platforms are available to implement public, private, consortium, or permissioned, permissionless Blockchain. Hyperledger, an open-source, permissioned, distributed ledger-based Blockchain, was hosted by Linux. This paper explores Hyperledger Fabric Private Blockchain Network (HFPBN). The architecture of HFPBN with its components and transaction flow is explored in detail. The Blockchain in HFPBN comprises multiple blocks that are linked to each other. The block elements are discussed in detail with their type and size, and after that, the total size of the block depending upon various parameters is calculated. Further, one application of Blockchain, i.e., Vehicular Ad-hoc Networks (VANETs), is discussed in this paper as a case study. The VANET application is being implemented on the Hyperledger Fabric platform. Formulas showing the dependency of various parameters like endorsement policy, number of transactions, and number of reads and writes on block size are derived and shown in their relationship through the graph for the VANET system. The impact of block size on various performance parameters like throughput, latency, memory, and CPU utilization for the VANET system is then analyzed using Hyperledger Caliper. An optimal required value of throughput and latency is achieved for Blockchain-based VANET. Also, the Hyperledger Fabric platform seems suitable for many applications as it creates separate Blockchain for different applications.