Vehicular Ad Hoc Networks Research Articles

A lossless quantization approach for physical-layer key generation in vehicular ad hoc networks based on received signal strength

Vehicular Ad Hoc Networks (VANETs) provide various benefits and play a crucial role in improving efficiency and ensuring human safety across different applications. However, these advantages also give rise to security challenges and privacy concerns, necessitating a thorough examination of security attacks. Current key generation schemes, particularly those based on the Physical-Layer Model (PLM), face limitations such as low key generation rates and inadequate randomness. This paper introduces an innovative key generation method that utilizes adaptive physical-layer techniques and lossless quantization. The method involves an eight-level quantization process, which enables precise granularity and adaptive selection of quantization thresholds to tailor the computation of Received Signal Strength (RSS) measurements to individual needs. The adaptive approach ensures the retention of information within RSS measurements, resulting in reduced bit disagreement rates and enhanced randomness of the generated keys. Simulated evaluations demonstrate the effectiveness of the proposed method, showing superior performance in terms of bit generation, entropy, and secrecy rates, while also minimizing the occurrence of unnecessary measurements. This advancement holds significant promise for strengthening the security framework within VANETs.

Discharging driven energy sharing protocol for V2V communication in vehicular energy networks

With the growing concern over range anxiety among electric vehicle (EV) owners due to limited battery capacity and sparse charging infrastructure, EV-to-EV (V2V) energy sharing emerges as a crucial solution to extend driving range. Leveraging the vehicular energy network (VEN) enabled by dynamic wireless power transfer (DWPT) technology, energy sharing among EVs in motion becomes feasible. However, the effective establishment of communication and identification of suitable V2V energy sharing pairs pose significant challenges, particularly for EVs with discharging demands. To address this challenge, we propose a new discharging driven energy sharing protocol based on vehicular ad-hoc networks (VANETs). Firstly, we present a routing approach for transmitting discharging information through VANETs, considering key factors such as distance, state of charge, number of neighbor vehicles, and vehicle speed. This routing scheme facilitates efficient relay node selection on road segments and intersections, ensuring optimal communication paths. Subsequently, we formulate a charging requester selection model to identify the most suitable requester for energy sharing. This model optimizes individual utility while accounting for the state of charge of the requesters, ensuring a comprehensive and inclusive approach to V2V energy sharing. Finally, we develop an acknowledgment message transmission scheme to ensure the completion of selection acknowledgment between discharging EVs and charging EVs. This scheme includes provisions for recovery in case of forwarding link failures, ensuring robust communication in dynamic vehicular environments. Extensive simulations conducted using network simulator 2 (NS-2) demonstrate the superior performance of the proposed protocol in terms of packet delivery ratio, end-to-end delay, and overall V2V energy sharing efficiency.

A Data Dissemination Algorithm Based on Maximization of Causal Path Entropy in Vehicular Ad Hoc Networks

A Data Dissemination Algorithm Based on Maximization of Causal Path Entropy in Vehicular Ad Hoc Networks

Privacy-preserving authentication approach for vehicular networks

Vehicle AdHoc networks have an important role in intelligent transport systems that enhance safety in road usage by transmitting real traffic updates in terms of congestion and road accidents. The dynamic nature of the vehicular AdHoc networks make them susceptible to attacks because once malicious users gain access to the network they can transform traffic data. It is essential to protect the vehicular ad hoc network because any attack can cause unwanted harm, to solve this it is important to have an approach that detects malicious vehicles and not give them access to the network. The proposed approach is a privacy preserving authentication approach that authenticates vehicles before they have access to the vehicular network thereby identifying malicious vehicles. The model was executed in docker container that simulates the network in a Linux environment running Ubuntu 20.04. The model enhances privacy by assigning Pseudo IDs to authenticated vehicles and the results demonstrate effectiveness of the solution in that unlike other models it boasts faster authentication and lower computational overhead which is necessary in a vehicular network scenario.

Comparative analysis of reactive routing protocols for vehicular adhoc network communications

In the recent past, vehicular adhoc networks (VANETs) have gained a lot of importance. The routing protocols play a vital role to deliver payloads from one vehicle to another one while they are moving at relative speeds. It is a challenging task to select a routing protocol for VANETs because of the uneven distribution and high mobility of vehicles. In this paper, we have analysed the two standard reactive protocols, adhoc on-demand distance vector (AODV) and ant colony optimization (ACO). The performance comparison of AODV and ACO routing protocols has been presented in this paper. The results show AODV performed better in terms of energy consumption and routing overhead. While considering the throughput, energy loss ratio, and delay ACO has performed. ACO resulted as upperhand.

SL-PPCP: Secure and Low-Cost Privacy-Preserving Communication Protocol for Vehicular Ad Hoc Networks

SL-PPCP: Secure and Low-Cost Privacy-Preserving Communication Protocol for Vehicular Ad Hoc Networks

Efficient Authentication Steps for Vehicle Transmission in Ad-Hoc Networks

Vehicular ad-hoc networks (VANETs) are underactive development, thanks in part to recent advances in wireless communication and networking technologies. The most fundamental part of VANET is to enable message authentications between vehicles and roadside units. Message authentication using proxy vehicles has been proposed to reduce the computational overhead of roadside units significantly. In this message authentication scheme, a proxy vehicle that verifies multiple messages at the same time improves roadside units’ efficiency. In this paper first, we show that the only proxy-based authentication scheme (PBAS) presented for this goal by Liu et al. cannot guarantee message authenticity and also it is not resistant to impersonation and modification attacks and false acceptance of batched invalid signatures. Next, we propose a new identity-based message authentication scheme using proxy vehicles (ID-MAP). Then guarantee that it can satisfy the message authentication requirements, existential unforgeability of underlying signatures against adaptability chosen message, and identity attack is proved under the Elliptic Curve discrete logarithm problem (ECDLP) in the random oracle model. It should be highlighted that ID MAP not only is more efficient than PBAS since it is pairing-free and identity based and also it does not use map-to-point hash functions, also, it satisfies the security and privacy requirements of VANETs. Furthermore, analysis shows that the required time to verify 3000 messages in ID-MAP is reduced by 76% compared to that PBAS.

An Integrated DQN and RF Packet Routing Framework for the V2X Network

With the development of artificial intelligence technology, deep reinforcement learning (DRL) has become a major approach to the design of intelligent vehicle-to-everything (V2X) routing protocols for vehicular ad hoc networks (VANETs). However, if the V2X routing protocol does not consider both real-time traffic conditions and historical vehicle trajectory information, the source vehicle may not transfer its packet to the correct relay vehicles and, finally, to the destination. Thus, this kind of routing protocol fails to guarantee successful packet delivery. Using the greater network flexibility and scalability of the software-defined network (SDN) architecture, this study designs a two-phase integrated DQN and RF Packet Routing Framework (IDRF) that combines the deep Q-learning network (DQN) and random forest (RF) approaches. First, the IDRF offline phase corrects the vehicle’s historical trajectory information using the vehicle trajectory continuity algorithm and trains the DQN model. Then, the IDRF real-time phase judges whether vehicles can meet each other and makes a real-time routing decision to select the most appropriate relay vehicle after adding real-time vehicles to the VANET. In this way, the IDRF can obtain the packet transfer path with the shortest end-to-end delay. Compared to two DQN-based approaches, i.e., TDRL-RP and VRDRT, and traditional VANET routing algorithms, the IDRF exhibits significant performance improvements for both sparse and congested periods during intensive simulations of the historical GPS trajectories of 10,357 taxis within Beijing city. Performance improvements in the average packet delivery ratio, end-to-end delay, and overhead ratio of the IDRF over TDRL-RP and VRDRT under different numbers of pairs and transmission ranges are at least 3.56%, 12.73%, and 5.14% and 6.06%, 11.84%, and 7.08%, respectively.

Reduction Jammer Detection and Recovery Algorithms for DSRC Safety Application in VANET

Intelligent Transportation Systems (ITS) encompasses technologies, services, and applications facilitating communication between vehicles (V2V) and between vehicles and fixed infrastructure (V2I and I2V). This mutual interaction constitutes a Vehicular Ad-Hoc Network (VANET) which supports a plethora of applications targeting critical transportation aspects, such as safety, mobility, and environmental considerations. Dedicated Short Range Communications (DSRC), operating on the 5.9 GHz band, is pivotal for such exchanges. We introduced an innovative algorithm designed to identify jamming attacks and transition the Safety Application to a secure fail-safe mode. This algorithm leverages a dual-metric strategy, incorporating both distance and PDR measurements. Field tests confirm that our algorithm adeptly recognizes the activities of deceptive jammers, ensuring a prompt shift of the safety application into its fail-safe state. This paper delves into these countermeasures, evaluating their efficiency via mathematical modeling, simulations, and on-ground testing. Findings acknowledge that these strategies bolster the reliability of safety applications in jamming scenarios. Furthermore, the approaches propounded align with ongoing standardization endeavors by relevant authorities, ensuring communication mediums remain unhindered.

Cryptography-based location privacy protection in the Internet of Vehicles

The evolution of the Internet of Things paradigm in recent years demonstrate a significant impact on the transportation sector, leading to the emergence of a new research field, known as the Internet of Vehicles (IoV). In the IoV, vehicles can exchange information with each other and with the roadside units making use of Vehicular Ad Hoc Networks (VANETs). As this technology reaches near-to-market maturity levels, several issues arise related to the protection of users’ privacy, while the interest of adversaries for such private user data in IoV environments gets stronger. This paper aims to present a review of the state-of-the-art techniques tackling the protection of location privacy in IoV environments, as well as experimental evaluation findings regarding the usage of various cryptographic algorithms for the protection of information exchange in these networks. In the conducted evaluations, the AES algorithm has been used as the main standard, which has been coupled with several other encryption/decryption algorithms, such as RSA, ECC and NTRU. The metrics used for the evaluation include measurements over the key generation process, the certificate generation, the encryption/decryption times, the signature generation/verification times, etc. Moreover, the size of messages in the negotiation, the pseudonym exchange and the new pseudonym enabling phases has been recorded, while the energy consumption in the exchange pseudonyms phase has also been measured. All previous experiments have been carried out mainly on NS-3 and SUMO open-source software aiming to have an estimation of how the aforementioned algorithms behave under constrained resources such as CPU usage and power.

Provably secure optimal homomorphic signcryption for satellite-based internet of things

Satellites have long been reliable and widely used mediums for communication. With recent advancements in the Internet of Things (IoT), satellite-based IoT has emerged, enhancing global connectivity by enabling real-time communication between remote sensing devices and central hubs through satellite links. However, secure and authenticated communication between Low Earth Orbit (LEO) satellites and resource-constrained ground devices remains underexplored. This paper introduces a novel homomorphic signcryption scheme designed for satellite IoT environments. Our scheme employs hyperelliptic curve cryptography and homomorphic encryption to facilitate the privacy-preserving transmission of aggregated data from multiple IoT devices to LEO satellites. We benchmark our homomorphic signcryption scheme against several established schemes tailored for different network environments, including for satellite networks, Identity-based Signcryption for Smart Grids, 3-Factor Authentication schemes and heterogeneous Vehicular Ad-hoc Networks (VANETs) encryption schemes. Our scheme demonstrates substantial improvements in computational efficiency, communication overhead, and storage capacity. It reduces computational costs by 90% to 99%, communication overhead by 41% to 92%, and storage demands by 62% to 93% compared to existing methods. These quantitative findings demonstrate the practical viability of our approach for satellite IoT deployments with limited resources. Additionally, the robustness of our approach is confirmed through formal security validation using BAN Logic and Scyther, which verifies its strong security, optimal performance, and privacy preservation capabilities, making it ideally suited for real-world satellite IoT systems.

Enhancement of VANETS Security against Gray Hole Attack with ANFIS-GWO and LBK

Vehicular Ad Hoc Networks (VANETs) have emerged as a critical component of intelligent transportation systems, facilitating real time communication among vehicles and infrastructure. However, the open and dynamic nature of VANETs makes them vulnerable to various security threats, among which gray hole attacks pose significant challenges. In gray hole attacks, malicious nodes manipulate or selectively drop data packets, disrupting communication and compromising network integrity. In response to these challenges, this paper proposes an innovative approach that integrates the Adaptive Neuro Fuzzy Inference System (ANFIS) with Grey Wolf Optimization (GWO). The main objective of this research is to develop a robust ANFIS GWO framework capable of identifying and mitigating gray hole attacks in real time VANET scenarios. Through extensive simulations and experiments, the performance of proposed technique in terms of average energy consumed, throughput, Packet Delivery Ratio (PDR) and packet drop is examined. The results demonstrate that the ANFIS GWO approach not only improves the resilience of VANETs against gray hole attacks but also guarantees reliable and secure communication among vehicles and infrastructure.

AN EFFECTIVE FRAMEWORK TO IMPROVE PERFORMANCE AND SECURITY IN INTELLECTUAL VANET

Vehicular Ad Hoc Networks, also known as VANETs, are an essential component in the development of intelligent transportation systems. They hold the promise of improving commuters' safety, efficiency, and convenience. The dynamic and decentralised character of VANETs, on the other hand, presents a number of issues, notably with regard to performance and security. a complete framework that is aimed to enhance the operation of VANETs and address the difficulties that have been identified. Intelligent routing algorithms, collaborationbased collision avoidance methods, and robust security protocols are the three primary components that make up the system. In the first place, intelligent routing algorithms make use of machine learning techniques in order to dynamically adapt to shifting traffic circumstances. This increases the efficiency of data transmission while simultaneously reducing delays. Whendetermining how to properly route data packets over the network, these algorithms take into consideration a variety of parameters, including the density of vehicles, traffic patterns, and congestion on the network.

IPCAS: An improved conditional privacy-preserving certificateless aggregate signature scheme without bilinear pairing for VANETs

Recently, Gong et al. proposed a Certificateless Aggregate Signature (CLAS) scheme (shorted by PCAS) with conditional privacy-preserving without bilinear pairing for Vehicular Ad-hoc Networks (VANETs). Unfortunately, after analyzing security, the PCAS scheme fails to satisfy the necessary unlinkability in VANETs and is vulnerable to the public key replacement forgery attack of the Type-I adversary. Subsequently, this article analyzes the causes of these two security issues in detail and proposes an improved CLAS scheme for VANETs (called IPCAS). Through security analysis, this article proves that the IPCAS is existentially unforgeable under the adaptive chosen message attacks against Type-I and Type-II adversaries in the random oracle model, and satisfies the necessary security and privacy requirements in VANETs, including unlinkability. Finally, the performance analysis results show that compared to PCAS for single message and n aggregate messages (n=1000), the computational overhead of IPCAS is reduced by 20.01% and 49.08% respectively, and the communication overhead is reduced by 18.75% and 28.14% respectively. Therefore, IPCAS not only makes up for the security vulnerabilities of PCAS but also has better performance.

Machine Learning-assisted Distance Based Residual Energy Aware Clustering Algorithm for Energy Efficient Information Dissemination in Urban VANETs

A Vehicular Ad-hoc Network (VANET) is an essential component of intelligent transportation systems in the building of smart cities. A VANET is a self-configure high mobile and dynamic potential wireless ad-hoc network that joins all vehicle nodes in a smart city to provide in-vehicle infotainment services to city administrators and residents. In the smart city, the On-board Unit (OBU) of each vehicle has multiple onboard sensors that are used for data collection from the surrounding environment. One of the main issues in VANET is energy efficiency and balance because the small onboard sensors can’t be quickly recharged once installed on On-board Units (OBUs). Moreover, conserving energy stands out as a crucial challenge in VANET which is primarily contingent on the selection of Cluster Heads (CH) and the adopted packet routing strategy. To address this issue, this paper proposes distance and energy-aware clustering algorithms named SOMNNDP, which use a Self-Organizing Map Neural Network (SOMNN) machine learning technique to perform faster multi-hop data dissemination. Individual Euclidean distances and residual node energy are considered as mobility parameters throughout the cluster routing process to improve and balance the energy consumption among the participating vehicle nodes. This maximizes the lifetime of VANET by ensuring that all intermediate vehicle nodes use energy at approximately the same rate. Simulation findings demonstrate that SOMNNDP improves Quality of Service (QoS) better and consumes 17% and 14% less energy during cluster routing than distance and energy-aware variation of K-Means (KM) and Fuzzy C-Means (FCM) called KMDP and FCMDP respectively.

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

Multi-TA model-based conditional privacy-preserving authentication protocol for fog-enabled VANET

The rapid growth of Vehicular Ad-hoc Networks (VANET), fueled by advancements in the Internet-of-Things, cloud computing, Intelligent Transportation Systems, and fog computing, has led to the introduction of fog node-based VANET to serve resource-constrained devices. In the traditional security models of VANET, due to the use of a centralized trusted authority, there is a chance of single-point-of-failure and service unavailable with the increased service access requests. Also, there was one-to-one communication between each roadside unit and trusted authority. This may increase the system complexity and increase the traffic load. To address these issues, a novel authentication protocol for fog-enabled VANET based on multiple trusted authority model is discussed which reduces the chance of service unavailability and single-point-of-failure as the entire traffic load is distributed among multiple sub-trusted authority. Due to the incorporation of fog node, a group of roadside units can be controlled centrally, where trusted authority does not need to perform individual authentication for each roadside unit. The proposed protocol's security is rigorously examined through both informal and formal security analysis. Additionally, the protocol exhibits enhanced security features, as demonstrated in a performance comparison section, showcasing its ability to meet the security and privacy requirements while incurring relatively low communication and computation and storage costs. Thus, the proposed protocol offers a secure and efficient authentication protocol for fog-enabled VANET.

Knowledge-based system and expectation-maximization to discovering causes of imperfect labels in vehicular networks clustering

This paper focuses on the development of a knowledge-based system for automatically diagnosing issues in Vehicular ad hoc networks (VANETs). VANETs enable communication between vehicles and infrastructure, enhancing road safety and efficiency through timely information exchange. The proposed system aims to efficiently maintain and ensure the continuity of network service by leveraging innovative pattern recognition methods tailored to VANETs. The automatic diagnosis problem in VANETs involves estimating the operating class of network components based on sensor observations. This entails associating sensor measurements with specific operating modes. By implementing condition-based preventive maintenance procedures, potential component failures can be detected early, mitigating network disruptions. Various approaches, such as expert systems, fault trees, network state models, and statistical learning through pattern recognition, can be employed to address this problem. This paper primarily focuses on the statistical learning approach, where a classification or regression function is learned from a set of examples to assign operation modes to new measurements. It discusses relevant metrics and preprocessing techniques to simplify the decision-making process. The diagnostic system's results are determined based on the formulation of the classification or regression problem. The learning base is constructed, and an appropriate classification method is selected to develop and validate the automatic diagnosis system. While non-parametric models like support vector machines are commonly used, this article emphasizes the significance of considering assumptions and leveraging additional information to enhance performance. It proposes a more specific formalization of the problem, integrating the unique characteristics of VANETs. The contributions of this article revolve around the theory of belief functions, a generative approach, and the utilization of parametric models defined using graphical models. Experimental studies conducted on artificial datasets have demonstrated the benefits of the semi-supervised approach within the context of VANET networks.

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

Service based VEINS framework for vehicular Ad-hoc network (VANET): A systematic review of state-of-the-art

Service based VEINS framework for vehicular Ad-hoc network (VANET): A systematic review of state-of-the-art