Vehicle Ad Hoc Networks Research Articles

Optimization of Contention Window and Relay Selection for Efficient Routing Using Fuzzy Logic Model

The rapid increase in vehicle density on roads owing to urbanization and motorization has led to increased risks of roadblocks, traffic jams, and accidents. To ensure the reliability of transportation, it is crucial to have stable and timely transmission of safety messages through Vehicle Ad-hoc Networks (VANETs). However, frequent vehicle movement and changes in the network topology may cause link breakage and packet loss. This paper proposes a solution that uses a fuzzy logic system in both the Medium Access Control (MAC) layer and the network layer to broadcast safety messages efficiently. The proposed rule-based model optimizes the Contention Window (CW) and relay selection process to adapt to different traffic conditions. The dynamic CW MAC (DYCW-MAC) model selects the optimum size of CW based on network parameters such as density, velocity, and link quality factor. For multi-hop communication, the model determines the next forwarding relay by considering factors such as direction, velocity difference, coverage factor, and Fast-Expected Transmission Count (F-ETX) between the sender vehicle and surrounding vehicles within its transmission range. The simulation results indicate that the DYCW-MAC model enhances the network throughput and decreases the average packet delay in comparison to other models.

Intelligent Decision Based Route Selection and Maintenance Approach Using AOMDV in VANET

Abstract: Vehicle Ad Hoc Network (VANET) is a dynamic network with a variety of applications in intelligent transport systems. There are several routing protocols for establishing paths between sending and receiving nodes. AODV is a reactive routing protocol that broadcasts a route request to all adjacent nodes, which forward the request to their neighbors. This process helps select the path from sender to receiver. The AODV protocol is the best routing protocol when you have a large number of communicating nodes. In this study, AODV and Ant Colony Optimization (ACO) techniques are applied to transfer data. Ant colony optimization algorithm is applied considering the initial coordinates of each node. Paths established using reactive routing protocols are likely to congest the network and also consume large amounts of network bandwidth. This research is based on using a multicast approach to establish a path from source to destination to reduce the potential for network congestion. It generates the shortest route and achieves higher throughput, less packet loss, and less delay than existing protocols.

Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.

Cyber Security Issues and Solution in Vehicular Networks

Vehicle Ad-hoc Networks (VANETs), which share traffic-related information between cars and infrastructure and aid to enhance driver safety and road safety, are capable of and viable for implementing Intelligent Transportation Systems (ITS). efficient traffic flow. Nonetheless, the confidentiality and privacy of such information is crucial for VANET due to the open wireless access channel. Attackers can compromise VANET security by capturing, intercepting, altering, replaying, or deleting traffic-related information. Thus, the hottest study field right now is assuring the security and privacy of traffic-related information in VANETs. Vehicle communication has been the subject of extensive effort in this situation. Yet, in terms of security needs, security assaults, and performance effectiveness, these duties fall short of adequately addressing security challenges.This white paper organises various authentication and privacy systems into categories and analyses their workings, advantages and disadvantages, security needs, threats, and performance metrics. Lastly, we chose the research inquiries that would be posted in the VANET security area.

Privacy-Preserving Deep Reinforcement Learning in Vehicle Ad Hoc Networks

The increasing number of road vehicles results in more fatalities and accidents. Thus, the manufacturing industry is working on driver safety to secure and safe transportation in Vehicle Ad hoc networks. In addition, the mobile vehicles run in the geographical zone and communicate roadside units over the wireless medium with a certain radius. The Internet of Vehicles has become a new network type where vehicles communicate with the application over public networks. This results in an increase in data exploration and threats related to network security. We propose the deep reinforcement learning method to sensitize the private information for a given vehicle connect over Vehicle Ad hoc networks, maintaining a balance between security and privacy through any sanitization process. Furthermore, we provide a set of recommendations and potential applications for the Vehicle Ad hoc networks as use cases.

ILL-IDS: An incremental lifetime learning IDS for VANETs

Vehicle Ad Hoc Network (VANET) is one of the most important approaches for intelligent vehicles to communicate under complex road conditions. However, as a VANET is working under wireless and complex conditions, it is under the threat of various malicious attacks. One efficient solution to counter those attacks is the Intrusion Detection System (IDS), which can detect intrusions to the VANET based on the statistical machine learning method. However, most IDS solutions have a problem that they can only detect the attacks which have already known when the IDS is designed/trained. For unknown attacks, most IDSs become inefficient. A blockchain-based Lifetime Learning IDS (LL-IDS) framework has been designed to address this problem. It applies a blockchain to store uncertain data that an IDS cannot decide and is highly likely to be a new type of attacks. With the help of traditional security agencies such as universities, these uncertain data can be labelled to train the IDS model. The incremental learning models are shown to have a great potential in this scenario. In this paper, we introduces a novel IDS named as incremental IDS based on LL-IDS. Numerical experiments show that the computational time consumption and web payload could be decreased by applying the ILL-IDS to a public VANET dataset.

DESC-IDS: Towards an efficient real-time automotive intrusion detection system based on deep evolving stream clustering

Controller area network (CAN) is a widely used communication protocol for in-vehicle networks. With the up-gradation of traditional vehicle ad-hoc networks (VANETs) to the internet of vehicles (IoV), the connectivity is incremental between the vehicular network and the outside world, making cyber-security become a stringent problem. Although existing machine learning-based methods for automotive intrusion detection are powerful, there are still limitations in detection performance and resistance attack types during the unsupervised learning process that lacks massive amounts of labels. Therefore, this paper proposes an in-vehicle intrusion detection system that incorporates a combination of sparse regularization convolutional auto-encoder (SRCAE) and streams clustering to construct a deep evolving stream clustering model, namely DESC-IDS. Specifically, the method encodes continuous messages as 2-D data frames, which are fed into the SRCAE built by the temporal convolutional network (TCN) to obtain a low-dimensional non-linear spatial–temporal mapping of the high-dimensional data. Thereafter, the stream clustering model can describe a contour baseline of normal communication messages by the spatial–temporal features. Based on this baseline, DESC-IDS can detect any abnormal changes in vehicular communication. In particular, this paper exploits the SRCAE to reconstruct message matrices, which are considered as variants of known attacks due to reconstruction deviation. The extensive evaluation results illustrate that the proposed model provides enough performance and real-time competitiveness in anomaly detection, with 96.44% accuracy on the HCRL intrusion dataset and 98.80% accuracy on the ORNL intrusion dataset. For the mixed attack of fabrication and masquerade, the proposed model achieves stable F1-scores of 93.48% and 86.99%, respectively. Moreover, the performance in unknown attacks is righteous with 98.43% accuracy and 97.15% F1-scores.

Stable routing protocol for unmanned aerial vehicle ad‐hoc networks based on DQN‐OLSR

In unmanned aerial vehicle ad-hoc network (UANET), the network topology changes with time due to the movement of the unmanned aerial vehicles (UAVs), which brings great challenges to the design of the routing protocol. In traditional routing protocols, when UANET topology changes, nodes cannot dynamically update neighbour nodes and topology information, and routing table calculation cannot accurately reflect the actual transmission path. As a result, network cannot meet the quality of service (QoS) requirements such as low end-to-end delay, high throughput and low packet loss rate. This paper proposes a dynamically optimized link state routing (OLSR) protocol based on Deep Q-Network algorithm (DQN-OLSR). In this protocol, each node first adjusts the sending interval of Hello messages adaptively in real time, according to the position and speed information of its neighbour nodes. Then the protocol uses the DQN algorithm to dynamically adjust the flooding interval of topology control (TC) messages to improve the routing update capability of nodes. The simulation verifies that the UANETs under this protocol have higher throughput and less packet loss rate than ad-hoc on-demand distance vector (AODV), grid routing protocol (GRP) and OLSR protocols, at different movement speeds in random waypoint (RWP) and random walk mobile models. Under nomadic as well as pursue mobile models, DQN-OLSR performs consistently with OLSR QoS performance, with the best performance among all four protocols. By further adding positioning errors to the nodes, it shows that the proposed protocol has good robustness, and the QoS performance degradation keeps within a low level.

Simulation Test Research on Typical Simulator NS-2 in Urban Vehicle Ad Hoc Network Based on Big Data

Abstract Vehicle ad hoc network (VANET) has gradually become a prominent research topic in the fields of wireless networks and intelligent vehicles. VANETs are unique mobile ad hoc networks with vehicles as their mobile nodes, presenting distinctive performance characteristics compared to traditional wireless self-organizing networks. In recent years, VANETs have gained significant attention in the wireless network and intelligent transportation domain. As an integral aspect of autonomous driving technology, vehicle-to-everything (V2X) communication spans multiple disciplines and is closely related to intelligent transportation, assisted driving, active safety, and smart vehicles. Evaluating VANET protocols and applications in real-world settings can be challenging. Therefore, utilizing simulation tools for VANET research is an effective approach. In this paper, we have designed and developed an optimized platform that uses IEEE 802.11a and IEEE 802.11p protocols for communication within a simulated urban traffic environment created with NS-2. The simulation results confirm the feasibility and rationale of applying the IEEE 802.11p protocol to wireless vehicular ad hoc networks. Within a distance of 300 m, at 0.0000 s, 14 key packets have not arrived in IEEE 802.11a, and 8 packets have not arrived in IEEE 802.11p; at 8.0000 s, 38 key packets have not arrived in IEEE802.11a, and 6 packets have not arrived in IEEE802.11p. Comparing the performance of IEEE 802.11a and IEEE 802.11p, the study concluded that the use of the 802.11p protocol in urban mobile environments can improve reliability and reduce average packet latency.

Secure authentication and privacy-preserving to improve video streaming vehicle ad-hoc network

In vehicular ad hoc networks (VANET), the privacy of vehicle data symbolizes a big challenge towards malicious attacks. On the other side, vehicles in VANET can play a staple role in monitoring the environment by sensing the surrounding environment, compute the sensing information, and transfer the results if needed to the authorized party. Most of the modern VANETs systems encrypt the information to prevent hacking it but mostly neglect the decryption that occurred when data need to re-processed. In this paper, we try to cover this weak point by using fully homomorphic encryption (FHE) because of its specifications. The proposed work focus on twofold: first, create secure authentication and permission management system. While the second is to preserve the privacy of vehicle data that transferred among VANET infrastructure. This scheme also deals with metric security features, such as data privacy, data integrity, and key management. In the experimental results, there is good advance in the fields of interest comparing with the related works.

A Secure and Efficient Decentralized Access Control Scheme Based on Blockchain for Vehicular Social Networks

The vehicular social network (VSN) is an emerging mobile communication system combining a vehicle ad hoc network (VANET) with a social network. It provides a new means of sharing, disseminating, and delivering data for passengers, drivers, and vehicles. However, a VSN may expose users’ private information, such as identities, location information, and trajectories, and tampering with shared data may lead to security and safety problems in vehicle systems. Considering the security and privacy preservation of shared data, we propose a lightweight decentralized multiauthority access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) and blockchain, by which a decentralized multiauthorization node supports vehicle users by performing lightweight calculations with the assistance of the vehicle cloud service provider (VCSP). We use blockchain to record storage and access transactions, achieving self-verification by users and tamper-resistance of ciphertexts. An improved smart contract reduces the workload of verification by users and achieves privacy preservation by hiding the policy. It supports user revocation and outsourced decryption, enabling more flexibility and better performance. A security and performance analysis shows that our scheme has clear advantages over existing schemes.

Optimized and Efficient Authentication in VANET using Blockchain

Vehicle ad hoc networks (VANETs) are the most exciting area of research in smart transport systems as they provide convenience and safety information to drivers. However, VANET's unique features make security, privacy, and trust management promising issues in the design of VANETs. It is a challenging problem to provide an effective anonymous authentication system in ad hoc vehicle networks (VANETs) with low computational cost. Blockchain technology in VANET offers a decentralized, secure, and reliable database, which is maintained by the network node. Earlier, vehicle authentication is performed whenever the vehicle enters the RSU range. In this paper, authentication will be done if a vehicle fails to travel a certain distance. This decreased number of authentications and time required for it leads to an increase in the efficiency of the system.

Secure and Efficient Message Authentication Scheme for 6G-Enabled VANETs

In 6G-enabled vehicle ad hoc networks (VANETs), the messages transmitted through wireless communication face security problems such as tampering and disclosure. In this paper, to ensure the security of transmitted messages and the privacy of vehicle users, we propose an anonymous and secure message authentication (ASMA) scheme. The ASMA scheme can realize message verification and conditional privacy preservation with a lower computation overhead, and its security does not depend on a tamper-proof device (TPD). As the numbers of vehicles and applications increase in 6G-enabled VANETs, the number of messages in the network increases greatly. One-by-one verification messages in the ASMA scheme cannot meet the strict low-latency requirements. To improve the efficiency of the ASMA scheme, we investigate a proxy-vehicle-assisted batch message authentication (PVBA) scheme. In the scheme, a proxy vehicle selection algorithm is designed to choose a certain number of proxy vehicles, and the message verification tasks are completed by a roadside unit (RSU) and the proxy vehicles synchronously. Performance analysis shows that in the case of large-scale messages, the PVBA scheme has lower verification delay than related schemes, and the verification efficiency is greatly improved.

Performance analysis for Bernoulli feedback queues subject to disasters: a system with batch Poisson arrivals under a multiple vacation policy

ABSTRACT We analyze an M/G/1 system with batch Poisson arrivals and instantaneous Bernoulli feedback, operating under a Multiple Vacation Policy. The system is subject to disasters that occur according to an independent Poisson process and are followed by (random) repair periods with general distribution. The analysis is carried out using the supplementary variable method. The Laplace transform of the time between two consecutive disasters is obtained and the existence of the stationary regime for the system is shown. Besides obtaining the stationary distribution for the number of customers in the system, we use the information regarding the rates of occurrence of various events provided by the supplementary variable solution to obtain a great variety of additional results. These include the Laplace transform of the busy period distribution and the probability that a customer completes service. We indicate areas of application of our model to real-life systems, in particular in Vehicle ad hoc Networks (VANETs), and we use the analytic results obtained to optimize such a system under a Quality of Service constraint. Finally, we analyze a variant of the system subject to disasters even when the server is not busy.

Privacy Preservation Authentication Model for a Secure Infrastructure over Vehicular Communications

Vehicle Ad-hoc Networks (VANET) are considered among recent wireless communication technologies. Nowadays, vehicles are no more than simple means of transport, they are endowed with a source of intelligence through their interaction with the road environment due to embedded equipment on board vehicles and integrated into stations along roads and highways. The mechanisms of security and protection of messages exchanged in VANET, thus preserving the privacy of users and satisfying the various security requirements, are a prerequisite for the deployment of vehicle networks. Increasingly, several research have been proposed to improve protocols for maintaining security and preserving privacy. This paper presents a hierarchical revocable infrastructure based privacy preservation authentication protocol for vehicles that involves authentication of each vehicle and the corresponding Road Side Unit (RSU) by a Certification Authority (CA). The proposed protocol used Elliptic Curve Diffie Hellman (ECDH) algorithm for reliable key exchange and Edwards-curve Digital Signature Algorithm (EdDSA) to speed up the execution of the authentication process especially at the key management level, message signing and verification of this signature. On the other hand, the creation of sub-lists of revoked certificates based on vehicle type makes it possible to minimize the response time by looking for a certificate if it is revoked or not. Our solution was checked by the security verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA), which indicated that it is a very secure level. Performance analysis illustrates that the protocol greatly saves computation resources.

SDN-assisted technique for traffic control and information execution in vehicular adhoc networks

Recently, applying Software-Defined Networking (SDN) in automotive industry has sparked a lot of interest due to its capacity to ensure focused organization across the board coupled with its vast versatility of isolating data from the control plane. However, the major concern in traffic network is that varied crisis and diversion messages are expected to be shared or brought into by the connected vehicles. Thus, disclosure of this information results in an evaluation of the steering and control of such messages. This research aims at employing SDN-assisted technique in heterogeneous vehicular organizations in executing information traffic-using optimal course determination and guiding in situations like a blockage in Vehicle Ad Hoc Networks (VANETs). To realize this, a workable traffic reproduction model is proposed and the experiments realized a re-enactment findings in terms of Round Trip Time (RTT) and Packet Delivery Ratio (PDR) and improves the conventional information flow structure.

TPM-Based Conditional Privacy-Preserving Authentication Protocol in VANETs

With the establishment of intelligent transportation systems (ITS), research on vehicle ad-hoc networks (VANETs) has played an irreplaceable role in improving traffic safety and efficiency. However, because the deployment of devices based on the IoV is in an open field, the IoV is extremely vulnerable to various attacks without security protection, e.g., remote intrusion, control, trajectory tracking, etc. In order to avoid the above-mentioned attacks and resource abuses, provably secure cryptography primitives are generally considered to guarantee and realize the security of VANETs. This paper proposes a TPM-based conditional privacy-preserving authentication protocol (T-CPPA) which achieves both the integrity and the authenticity of the message/instruct content. The vehicle’s privacy is protected by embedding the system master private key into the trust platform module (TPM) which is responsible for generating pseudonyms and signature keys. The authenticity of message content is ensured by calculating message similarity in a cluster-based model. We give the concrete construction of our T-CPPA authentication scheme in symmetric bilinear groups and design a batch validation algorithm to improve efficiency. Security analysis shows that our scheme can resist various traditional attacks in VANETs, and the experimental results indicate that our scheme is efficient and useful in practice.

A Novel Secured Multi-Access Edge Computing based VANET with Neuro fuzzy systems based Blockchain Framework

In vehicle ad-hoc networks, the progression of wireless communication technology to 6G, overcomes storage, processing, privacy, and power limits to create an efficient and intelligent next generation transportation system. Vehicular ad hoc network may now offer remarkable availability, reliability, and throughput using 6G technology. However, the VANET system’s data should be protected. This paper proposes an effective batch authentication and key exchange technique to avoid contact with hostile vehicle users. Moreover, three types of systems are proposed: PKI, ID-based, and MAC-based. The neuro-fuzzy inference technique was used to predict VANET security ratings. The Homogeneous Discrete-Time Markov Chain model is used to secure data transit. Additionally, this research examined the work from a blockchain perspective combined with MEC. There are 3 level to comprise the architecture: perception, edge computing, and services. Throughout the blockchain transmission process, the first layer make certain the security of VANET data. The perception layer makes use of edge computing and cloud services on the edge. The service layer protects data by using both traditional cloud storage and blockchain technology. The lowest layer of the system architecture is dedicated to the throughput and quality of service requirements of MEC users. The primary challenge is achieving consensus across blockchain nodes while maintaining the MEC system’s and blockchain’s performance. To simulate the joint optimization problem, a Markov decision process with reward function is utilized. The simulation results are conferred to illustrate the validity of study assertions.

Provable Secure and Lightweight Vehicle Message Broadcasting Authentication Protocol with Privacy Protection for VANETs

With the emergence of intelligent transportation and the widespread use of vehicle network equipment, the vehicle ad hoc network (VANET) is widely used in the communication among participating entities to obtain traffic information, such as speed, traffic congestion, road conditions, and accident. In VANET, the secure and efficient message broadcasting protocol can effectively reduce the message transmission delay, to meet the requirements of openness, real-time, and high-speed mobility in the VANET environment. However, most related researches mainly rely on third parties, such as TA and RSU, to ensure the identity authentication of vehicles and the security of message transmission, which are not suitable for infrastructure-less scenario. Therefore, designing a protocol for decentralized message broadcasting, identity authentication without a third party, and the safe message transmission is meaningful. In this study, we propose a lightweight vehicle message broadcasting authentication protocol, which realizes the identity authentication of the message broadcasting vehicle and vehicle-to-vehicle (V2V) key agreement without the need of a trusted third party. In our protocol, the vehicle can verify the identity of the sending vehicle to identify the reliability of the message source and prevent malicious messages. Meanwhile, the vehicle can update its pseudonym identity to resist tracking attacks, but the trusted authority can always track the real identity of the malicious message sending vehicle according to the sent messages. The proposed protocol is proved secure by formal security proof. In addition, our scheme provides better security and computational efficiency over others by comparing with related schemes.

Data Transmission Control of Vehicle Ad Hoc Network in Intelligent Transportation Systems for Smart Cities

The superposition of various application data streams in smart cities can intensify the load of vehicular networks in intelligent transportation systems, which can have an impact on the popularity of smart cities. To improve the performance of large amount of data transmission in telematics, this study proposes a scheme to determine the network state using congestion parameters and routing parameters and matches different data transmission amounts according to different states of the network. The scheme first considers the possibility of the network congestion state. Once congestion is judged to occur, the amount of data sent is reduced, and reducing the data backlog can further mitigate the possibility of congestion formation on the network. Secondly, after rejecting the possibility of congestion, the routing situation of the network needs to be judged, in such cases, whether a change in vehicles in the multi-hop path leads to a path change or an interruption of the data transmission path. Congestion parameters and routing parameters evaluate the state of the network, and the size of the congestion window is appropriately limited by the routing parameters to prevent excessive data volume from causing backlogs in the vehicular network. Experimental simulations show that the proposed scheme exhibits good performance in both linear and crossover vehicular networking scenarios. The research results provide a useful reference for the data transmission of telematics in smart cities.