Vehicular Ad Hoc Networks Research Articles

An efficient multi-objective UAV assisted RSU deployment (MOURD) scheme for VANET

A Roadside Unit (RSU) serves as essential infrastructure in Vehicular Ad Hoc Networks (VANETs) that supports the goals of Intelligent Transportation Systems (ITS) by providing safety services, shared storage, and enhanced internet connectivity to vehicular users, drivers, and pedestrians. Additionally, the efficiency of VANETs, concerning network service utility and latency, depends on the relative positioning of these RSUs within the network topology. Most existing RSU deployment approaches deal with a single objective, either enhancing network service utility or minimizing the latency. For instance, some studies suggest deploying RSUs in high-traffic road segments that enhance network service utility but lead to higher latency. Conversely, some suggest deploying the RSUs in low-traffic road segments that minimize the network latency, but there will be low network service utility. Hence, there exists a trade-off between these two conflicting objectives in VANETs, and none of the studies address both objectives simultaneously. To achieve the balance between these two objectives, this paper proposes a Multi-Objective UAV assisted RSU Deployment (MOURD) scheme that leverages the Unmanned Aerial Vehicles (UAVs) for VANET efficiency. The MOURD scheme statically places RSUs in high-traffic road segments and dynamically dispatches the UAVs in low-traffic road segments to facilitate seamless network coverage and minimize the overall network latency. The simulation results on the road network of Delhi, India, demonstrate the effectiveness of the proposed MOURD scheme compared to other benchmark RSU & UAV deployment approaches. MOURD scheme outperforms on an average of 17.42%, 13.29%, 15.67% and 6.23% in terms of vehicle connection time, packet delivery ratio, throughput, and latency, respectively.

Blockchain-Enabled Trust Management With Location Privacy Preservation in Vehicular Ad Hoc Networks

Blockchain-Enabled Trust Management With Location Privacy Preservation in Vehicular Ad Hoc Networks

Fractional Jaya election‐based optimization enabled routing and charge scheduling for electric vehicle in cloud‐assisted Vehicular Ad Hoc NETwork

SummaryElectric vehicles (EVs) are the emerging environmentally friendly approach that is used to minimize greenhouse gases and carbon dioxide (CO2) emissions in the atmosphere. A clear strategy is required for scheduling charging stations (CS) to EVs based on their applications. In this research work, a robust routing and effective charge scheduling approach are devised using a cloud‐assisted Vehicular Ad Hoc NETwork (VANET) for charging EVs. Here, the multi‐objectives, like predicted traffic density, battery power, and distance, are used to identify the optimal routing of EV to CS. The predicted traffic density is evaluated using Deep Long Short Term Memory (DLSTM) and is trained using a developed Jaya Election‐Based Optimization Algorithm (JEBOA), which is the incorporation of Jaya Optimization (Jaya) and Election‐Based Optimization Algorithm (EBOA). Next to optimal routing, the charge scheduling process is carried out using the Fractional Jaya Election‐Based Optimization Algorithm (Fractional JEBOA) by considering the priority, response time, and latency of the EV. The designed Fractional JEBOA is the integration of Fractional Calculus (FC) and the developed JEBOA. Moreover, the various evaluation metrics are considered to calculate the performance of the designed method, which attained a delay of 0.243 ms, distance of 35 km, power of 95 W, response time of 0.441 s and traffic density of 0.664.

Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks.

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

A privacy-preserving data aggregation system based on blockchain in VANET

In the realm of vehicular ad hoc networks (VANETs), data aggregation plays a pivotal role in bringing together data from multiple vehicles for further processing and sharing. Erroneous data feedback can significantly impact vehicle operations, control, and overall safety, necessitating the assurance of security in vehicular data aggregation. Addressing the security risks and challenges inherent in data aggregation within VANETs, this paper introduces a blockchain-based scheme for secure and anonymous data aggregation. The proposed scheme integrates cloud computing with blockchain technology, presenting a novel blockchain-based data aggregation system that robustly supports efficient and secure data collection in VANETs. Leveraging key escrow resilience mechanisms, the solution ensures the security of system keys, preventing the security problems caused by keys generated by third parties alone in the past. Furthermore, through secondary data aggregation, fine-grained data aggregation is achieved, providing effective support for cloud services in VANETs. The effectiveness of the proposed scheme is confirmed through security analysis and performance evaluations, demonstrating superior computational and communication efficiency compared existing alternatives.

Main Primitive and Cryptography Tools for Authentication in VANET Environment: Literature Review

Vehicular ad hoc networks (VANETs) provide the potential to improve transportation efficiency by facilitating the sharing of traffic information among vehicles. Acceptance of VANET depends on communication speed and accuracy as well as privacy protection guaranteeing an individual's safety. Vehicle authentication is necessary to ensure message correctness. This necessitates the implementation of an effective privacy-preserving authentication scheme, as well as the need for both secrecy and timebound delivery of messages. Various privacy-preserving authentication schemes have been suggested to guarantee the integrity of messages in communications. However, most of the schemes are not able to solve issues related to computing costs, communication, security, privacy, threats, and vulnerabilities. In this review, we focus on cryptographic strategies that are suggested to accomplish privacy and authentication, such as identity-based, public key cryptography-based, pseudonym-based, and blockchain-based schemes. We provide a thorough analysis of schemes, including their categorizations, advantages, and drawbacks. The study demonstrates that the majority of current authentication techniques necessitate trusted authorities that lack transparency in their operations. Additionally, authentication process incurs substantial computational and communication overhead, leading to a considerable impact on the timely delivery of messages. More efforts are required to enhance the development of efficient authentication schemes in VANETs.

Evaluating End-to-End Delay in Road-Based Routing Protocols for VANETs with Snake Optimization

Vehicular Ad hoc Networks (VANETs) require efficient routing protocols due to their dynamic network topologies and high mobility. This paper proposes a Road-Based Vehicular Traffic (RBVT), which combines reactive (RBVT-R) and proactive (RBVT-P) routing protocols to improved the performance. We evaluate the impact of RBVT on Average End-to-End Delay (AEED) by comparing it with existing protocols such as AODV, DSR, GPSR, OLSR, DSDV, and WRP. Furthermore, we integrate Snake Optimization Algorithm (SOA) with RBVT to optimize routing and reduce AEED. Simulations demonstrate that SOA-RBVT significantly reduces AEED compared to baseline protocols, especially in dense VANET scenarios.

Prediction of Traffic Flow in Vehicular Ad-hoc Networks using Optimized Based-Neural Network

Traffic flow prediction is crucial for managing traffic, reducing pollution, ensuring public safety, and is a vital element of Intelligent Transportation Systems. Furthermore, the variables that influence traffic patterns (such as public events, road closures, and accidents) are predominantly unforeseen, rendering the prediction of traffic flow an intricate undertaking. The objective of this research is to enhance the accuracy of traffic flow forecast by employing a novel strategy that utilizes feedforward Neural-Networks and the Quasi-Newton method for optimization. The proposed strategy decreases the error factor based on the Lagrange multiplier and Jacobian vector. This enhancement has resulted in expedited convergence throughout the process of learning. The sample was chosen using datasets provided by the Traffic Monitoring System, specifically from Highway England, Performance Measurement System, and Maryland 511, for the year 2023. In order to assess the proposed model, the research outcomes are juxtaposed with other established prediction methodologies. The study revealed that the research model that was established performed better than previous prediction strategies based on measures of mean absolute error and root mean squared error.

Evaluating the Performance of ETSI-ITS Multi-Stack Protocols for V2V Communication in VANETs: A Simulation Study

The research evaluates various multi-stack protocols for Vehicular Ad-hoc Networks (VANETs), focusing on Vehicle-to-Vehicle (V2V) communication scenarios with Emergency Vehicle (EV) simulations. The study uses the ns-3 network and SUMO (Simulation of Urban MObility) traffic simulators to test these protocols in diverse scenarios, including fluctuating data rates and dense network conditions. By implementing the IEEE 802.11p protocol alongside vehicular message dissemination stacks compliant with ETSI (European Telecommunications Standards Institute) ITS (Intelligent Transport Systems) standards, the study performs simulation experiments with varying vehicle counts, ranging from 20 to 35. It employs two distinct data rate configurations while maintaining a constant transmission power of 23 dBm. The results indicate a decline in the average Packet Reception Ratio (PRR) as vehicle density increases, indicating heightened contention and interference. At the same time, there is an observed increase in average latency, contributing to increased message transmission and reception delays. The quantitative analysis demonstrates an inverse relationship between the average PRR and the total vehicle count when the SEND_CAM message is enabled. On the other hand, disabling SEND_CAM maintains a relatively consistent average PRR across scenarios. Additionally, a positive correlation between vehicle count and average latency underlines the impact of network congestion and interference on communication efficacy within VANETs. Despite suboptimal PRR values falling between 41% and 47%, latency performance remains satisfactory, with average latency durations ranging from 0.154 s to 0.187 s. Notably, the SEND_CAM parameter status shows negligible impact on protocol performance, suggesting that network density plays a more pivotal role. Finally, this study offers valuable insights into the trade-offs and challenges of multi-stack protocols in V2V communication within VANETs. Further optimization efforts are recommended to improve packet reception ratios, especially in high-vehicle-density environments, while maintaining acceptable latency levels. These findings contribute to the ongoing efforts to enhance the reliability and efficiency of communication protocols in VANETs, thus advancing the development of intelligent transportation systems. The study's quantitative protocol performance analysis under varying network conditions provides valuable guidance for optimizing V2V communication deployments in VANETs.

A Comprehensive Analysis of Deep Learning and Swarm Intelligence Techniques to Enhance Vehicular Ad-hoc NETwork Performance

A Comprehensive Analysis of Deep Learning and Swarm Intelligence Techniques to Enhance Vehicular Ad-hoc NETwork Performance

On a security scheme against collusive attacks in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) offer a promising solution to bring drivers comfortable driving experiences and also improve road safety in intelligent transportation systems, but also faces many security issues. Collusive attack is one of the most challenging threats in VANETs because it violates the fundamental assumption made by VANET-based applications that all received information be correct and trustworthy. Collusive attackers can not only generate and send false or forged messages, but also purposely manipulate the reputation value of normal or malicious vehicular nodes. To address these issues, we analyze the behaviors characteristics of collusive attacks and propose a generic, lightweight, and fully distributed detection scheme against collusive attacks in VANETs. This scheme integrates two methods to identify different collusive attacks for fraud reputation and fraud message, respectively, as well as an incentive method to restrain collusive nodes. Simulation-based experiments are conducted and the results illustrate the superiority of the proposed security scheme over state-of-the-art methods.

Adaptive multidimensional trust-based recommendation model for peer to peer applications

Abstract In today’s world, the widespread utilization of services such as Nearby Share, Near Field Communication (NFC), and Wi-Fi Direct for deploying various applications has significantly bolstered the demand for reliable and secure distributed ad-hoc peer-to-peer networks. Yet, ensuring the trustworthiness of participating nodes remains a significant challenge. Trust among nodes plays a pivotal role in collaborative network applications, especially in environments like Mobile Ad-hoc Networks and VANET (Vehicular Ad-hoc Networks). Evaluating the trustworthiness of nodes is essential for promptly identifying misleading entities, thereby preemptively preventing their involvement in ongoing transactions. Attributes or characteristics exhibited by nodes, such as honesty, selfishness, or malicious behavior, serve as key factors in trust computation. The effectiveness of trust evaluation directly influences the encouragement of honest nodes and the deterrence of malicious ones, thereby nurturing a healthy and competitive network ecosystem. Recognizing the dynamic nature of network environments, trust computation methods must be adaptable and diverse. The adaptive multidimensional trust (AMT) model introduced in this article goes beyond simple reputation assessment. It offers three distinct methods such as Direct Trust ( Direct Trust ) \left({{\rm{Direct}}}_{{\rm{Trust}}}) , multiple security parameters, identification of qualified recommenders, which got selected dynamically as per change in trust ratings of peers. AMT advocates for an incentive-driven approach to identify legitimate peers, monitoring gradual increases in their performance ratings, whereas, spikes in performance alert to potential colluding peers or nodes displaying erratic behavior. This article evaluates the effectiveness of the AMT through a case study focused on an E-commerce application. It scrutinizes the model’s performance across different percentages of malicious nodes within the network, providing a thorough analysis and discussion of the results based on the trust value of malicious and benign peers and efficiency by selecting genuine service for transaction.

Markov-reward based estimation of the idle-time in vehicular networks to improve multimetric routing protocols

Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as Tidle. This current study focuses on modeling and analyzing the influence of Tidle on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute Tidle. This equation provides a quick evaluation of Tidle, facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our Tidle evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our Tidle metric. Simulation results demonstrate that integrating Tidle as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET

Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this study, DT has integrated with cutting-edge technologies such as Software-Defined-Network (SDN) and geographically distributed computing (fog computing) to elevate the position-based routing behavior in two directions: selecting the optimal next-hop and taking control of periodic messages (beacon transmission). The nature of routing decisions can be complex due to constant movement and frequent position changes of vehicles in the VANET system. Merely selecting the next-hop vehicle without considering its stability and predictability can result in suboptimal routing choices. To this end, SDN based on Reliable Emergency Message Routing Schema (SDN-REMR) is introduced. This schema utilizes DT and aims to leverage the behaviors of moving vehicles to enhance the delivery of EM by providing a reliable routing mechanism. To eliminate unstable neighbors from the list of potential next-hops, SDN-REMR forecasts the relative positions of surrounding vehicles using Euclidean distance and position information. Moreover, SDN-REMR uses the movement information collected from vehicles (such as position, speed, and power consumption) to reduce the possibility of a link disruption and select the best next-hop for reliable communication. SDN-REMR enables the Store-Carry-Forward (SCF) approach to ensure packets are successfully forwarded to the destination, even in challenging network conditions. This control strategy aims to strike a balance between maintaining reliable communication and minimizing congestion in the network. The experimental results prove that there are 42.6%, 76.22%, 76.22%, and 22.2% enhancement in terms of network throughput, total delivered packets, number of dropped packets, and routing overhead.

Security, Privacy, and Decentralized Trust Management in VANETs: A Review of Current Research and Future Directions

Vehicular Ad Hoc Networks (VANETs) are powerful platforms for vehicular data services and applications. The increasing number of vehicles has made the vehicular network diverse, dynamic, and large-scale, making it difficult to meet the 5G network’s demanding requirements. Decentralized systems are interesting and provide attractive services because they are publicly available (transparency), have an append-only ledger (robust integrity protection), remove single points of failure, and enable distributed key management and communication in a peer-to-peer network. Researchers dedicated substantial efforts to advancing vehicle communications, however conventional cryptographic mechanisms are insufficient which enabled us to look at decentralized technologies. Therefore, we revisit decentralized approaches with VANETs. Endpoint devices hold a wallet which may incorporate threshold key management methods like MPC wallets, HD Wallets, or multi-party threshold ECDSA/EdDSA/BLS. We also discuss trust management approaches and demonstrate how decentralization can improve integrity, security, privacy, and resilience to single points of failure. We also conduct a comprehensive review, comparing them with current requirements, and the latest authentication and secure communication architectures, which require the involvement of trusted but non-transparent authorities in certificate issuance/revocation. We highlight the limitations of these schemes from PKI deployment and recommend future research, particularly in the realm of quantum cryptography.

CN+: Vehicular Dataset at Traffic Light Regulated Intersection in Bremen, Germany

Vehicular Ad-Hoc Networks (VANETs) were introduced to avoid vehicular-related accidents and to improve the safety of both vehicular passengers and other road users. In VANETs, the vehicles are expected to communicate with neighbouring vehicles to increase awareness about the surrounding by using V2V (vehicle-to-vehicle) communication links. Since the introduction of VANETs, much research has focused on developing state-of-the-art algorithms to increase safety. However, real-world testing of these developed algorithms has become challenging due to the required high cost and multiple practical reasons. Therefore, simulation-based testing is commonly used for VANETs related applications. Using real datasets inside a simulation can significantly increase the results’ accuracy and help to achieve realistic results. In this study, we present a dataset called ’CN+’, which consists of more than 25,000 vehicles collected over 32 hours at a signalized intersection in Bremen, Germany. paper

Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning

Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.

CMAF: Context and Mobility-Aware Forwarding Model for V-NDN

Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to the so-called table of neighbors (NT) from which a next-hop is selected. However, NTs also require updating. For this purpose, some solutions resort to broadcasting beacons. We propose a Context- and Mobility-Aware Forwarding (CMAF) strategy that resorts to a Short-Term Mobility Prediction—STMP—algorithm, for keeping the NT updated. CMAF is based in Named Data Networking (NDN) and works in two modes, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V CMAF leverages the overheard packets to extract mobility information used to manage NT and feed the STMP algorithm. V2I CMAF also uses a controlled and less frequent beaconing, initially from the Road-Side Units (RSUs), for a further refinement of the predictions from STMP. Results from extensive simulations show that CMAF presents superior performance when compared to the state of the art. In both modes, V2V and V2I (with one beacon broadcast every 10 s) present 5–10% higher Interest Satisfaction Ratio (ISR) than those of CCLF for the same overhead, at a cost of 1 s of increased Interest Satisfaction Delay (ISD). Moreover, the number of retransmissions of CMAF is also comparatively low for relatively the same number of hops. Compared to VNDN and Multicast, CMAF presents fewer retransmissions and 10% to 45% higher ISR with an increased overhead of about 20%.

Trajectory tracking attack for vehicular ad‐hoc networks

AbstractMaintaining user privacy and security is a critical concern in vehicular ad hoc networks (VANETs). However, prior research has neglected the study of matrix recovery attack methods in VANETs and the challenge of reducing the number of roadside units (RSUs). In this article, we formulate a path recovery strategy using matrix recovery techniques from an adversarial view. Subsequently, the challenge of minimizing RSUs while monitoring all user vehicles in a region is converted into a set cover problem. We introduce a heuristic algorithm that utilizes clustering to address this issue. To minimize matrix recovery errors, a Kalman filter based method is integrated to enhance the performance. This paper also presents an initial deployment of path recovery attacks, maintaining effectiveness even with certain defense mechanisms in place. Furthermore, we conduct simulation experiments to evaluate the effectiveness of the proposed attack strategy. The simulation results demonstrate the performance across various dimensions. Finally, the results show that the success rate of our proposed counter‐defense strategy in overcoming user defenses surpasses 50%.