V2V Networks Research Articles

IBTD: A novel ISAC beam tracking based on deep reinforcement learning for mmWave V2V networks

AbstractBeam tracking is commonly employed in millimetre wave (mmWave) based vehicle‐to‐vehicle (V2V) networks to align the beams towards the intended targets and compensate for the path loss of mmWave signals. To mitigate the high latency issue arising from the tracking processes, integrated sensing and communication (ISAC) technology leverages the echo signal to sense the motion parameters of the target, achieving low‐latency beam tracking without requiring pilot and uplink feedback. Existing studies mainly focus on utilizing ISAC for beam alignment to track the target, without integrating beam tracking with resource allocation. In this paper, we propose the ISAC beam tracking based on deep reinforcement learning (IBTD) algorithm to address this problem. Specifically, we introduce the concept of packet age to measure communication performance. To achieve accurate beam tracking and optimize the transmit power, we integrate the sensing results, such as the position and velocity of the target vehicle, along with the buffer pool status information, with deep reinforcement learning (DRL) to select an appropriate policy. Furthermore, we consider the effect of inter‐vehicle distance and incorporate the changing of tracking targets into the DRL‐based policy. Simulation results demonstrate that the proposed IBTD algorithm achieves lower packet age and transmit power consumption compared to the baseline algorithms.

Enhancing vehicular NOMA communication security through reconfigurable intelligent surfaces

Physical layer security (PLS) aims to ensure the confidentiality and authenticity of transmitted data by capitalizing on the inherent randomness of wireless channels. Owing to the popularity of intelligent transportation systems (ITSs), PLS research has sparked renewed interest in the wireless research community. This paper investigates the performance of secure communication in the context of a vehicle-to-vehicle (V2V) communication scenario by employing a reconfigurable intelligent surface (RIS). Further, we introduce the concept of non-orthogonal multiple access (NOMA) to reduce latency and improve communication efficiency in V2V networks. This study aims to comprehensively analyze secrecy performance, encompassing parameters like average secrecy capacity (ASC), secrecy outage probability (SOP) and probability of non-zero secrecy capacity (PNZSC). Our research aims to highlight the efficacy of RIS in providing secure and reliable communication within V2V NOMA networks. Ultimately, our study contributes to advancing secure communication protocols in intelligent transportation systems.

A Novel Approach for Efficient Resource Allocation in 6G V2V Networks using Neighbor-Aware Greedy Algorithm and Sweep Line Model

A Novel Approach for Efficient Resource Allocation in 6G V2V Networks using Neighbor-Aware Greedy Algorithm and Sweep Line Model

Moving target defense approach for secure relay selection in vehicular networks

Ensuring the security and reliability of cooperative vehicle-to-vehicle (V2V) communications is an extremely challenging task due to the dynamic nature of vehicular networks as well as the delay-sensitive wireless medium. In this context, the moving target defense (MTD) paradigm has been proposed to overcome the challenges of conventional solutions based on static network services and configurations. Specifically, the MTD approach involves the dynamic altering of network configurations to improve resilience to cyberattacks. Nevertheless, the current MTD solution for cooperative networks poses several limitations, such as that they require high synchronization modules that are resource-intensive and difficult to implement; and they rely heavily on attack-defense models, which may not always be accurate or comprehensive to use. To overcome these challenges, the proposed approach introduces an adaptive defense strategy within the MTD framework. This strategy proposes an intelligent spatiotemporal diversification-based MTD scheme to defend against eavesdropping attacks in cooperative V2V networks. It involves altering the system configuration spatially through relay selection and adjusting the percentage of injected fake data over time. This approach aims to balance reducing intercept probability while ensuring high throughput. Our methodology involves modeling the configuration of vehicular relays and data injection patterns as a Markov decision process, followed by applying deep reinforcement learning to determine the optimal configuration. We then iteratively evaluate the intercept probability and the percentage of transmitted real data for each configuration until convergence is achieved. To optimize the security-real data percentage (S-RDP), we developed a two-agent framework, namely MTD-DQN-RSS & MTD-DQN-RSS-RDP. The first agent, MTD-DQN-RSS, tries to minimize the intercept probability by injecting additional fake data, which in turn reduces the overall RDP, while the second agent, MTD-DQN-RSS-RDP, attempts to inject a sufficient amount of fake data to achieve a target S-RDP. Finally, extensive simulation results are conducted to demonstrate the effectiveness of our proposed solution, which improved system security compared to the conventional relay selection approach.

Reliability Assessment of Vehicle-to-Vehicle Communication Networks through Headway Distribution and Information Propagation Delay

Several developed vehicle spaces and time headway distribution models in traffic flow theory have been widely used in the literature, reflecting the primary uncertainty in drivers’ car-following movements and explaining the traffic flow stochastic features. Moreover, effective vehicle-to-vehicle (V2V) communication is a key to decentralizing traffic information systems. Accurate vehicle headway distribution estimation will ensure reliable communication and benefit passengers’ safety and comfort. Consider several proposals for headway distribution in the literature; this paper studies the effect of space-headway distribution on information propagation delay in assessing the reliability of the V2V communication networks. We utilize the properties of reliability measurement for headway data by introducing the quasi-maximum likelihood estimator (QMLE) to measure the effects that different headway models have on estimating the parameters of headway distribution in a V2V communication network. The statistical analysis is then applied to the real Next-Generation Simulation (NGSIM) data. It validates the proposed methodology and formulations by measuring the effects of model selection on headway data and information propagation delay on the reliability of the V2V network. The results show that the effect of headway distribution when the vehicle’s transmission range is smaller than the road segmentation is not negligible, especially when cars are very distant. Based on our results, we recommend new metrics based on Kullback–Leibler divergence for model selection of headway data, thereby enhancing the reliability of the V2V network.

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding.

Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.

Online MEC Offloading for V2V Networks

As an enabling technology for vehicle-to-vehicle (V2V) networks, multi-access edge computing (MEC) provides a feasible platform for sharing power and resources, and offloading some of the computation-intensive tasks between vehicles. This, however, is challenging with the unpredictable variations in road traffic conditions and vehicle mobility in MEC-enabled V2V networks. Consequently, such computation task offloading can be easily disrupted, which may require frequent switching of task offloading between vehicles and degrade the Quality of Service (QoS). In this paper, we focus on the computation offloading problem under unstable connections in MEC-enabled V2V networks. We first model this as a distributed online service optimization problem, which is proved to be NP-hard. In order to minimize the out-of-service time (i.e., the service mismatching, switching and compromise time), we propose a distributed Online Instability-aware Computation Offloading (OICO) heuristic algorithm to improve the service efficiency and quality. Specifically, in order to minimize the service mismatching rate, we design an efficient Service Path Matching (SPM) algorithm for matching pairs of customer vehicles (which require offload computing services) and server vehicles (which provide edge computing services) that share the longest matching path. We evaluate OICO through real-world traces, i.e., GAIA open dataset from DiDi. Extensive simulation results demonstrate that OICO can increase the service matching rate by 25% and reduce the power consumption by about 54% per customer vehicle compared with the existing schemes.

A Robust CACC Scheme Against Cyberattacks via Multiple Vehicle-to-Vehicle Networks

Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks. Here, we address the problem of increasing the robustness of CACC schemes against cyberattacks by using multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication channels to create redundancy at the receiver side. We propose a data fusion algorithm to estimate of the true acceleration command, and isolate compromised channels. Finally, we propose a robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> controller that reduces the joint effect of fusion errors and sensor/channel noise in the platooning performance (tracking performance and string stability). Simulation results are presented to illustrate the performance of our approach.

Design of Internet of Vehicles Routing Protocol based on Prior Probability

With the continuous development of the interactive system and the wireless heterogeneous network, the intelligent transportation system plays a key role in the modern transportation, especially in the construction of the Internet of vehicles. The important position of routing protocol in building the vehicle communication network is self-evident. V2V (Vehicle to Vehicle) network also has irreplaceable advantages in eliminating traffic accidents and reducing the harm of traffic accidents. Therefore, it is very necessary to study the optimization of the routing protocol. However, in the previous vehicle since the organization network routing protocol research, mostly focus on the method of data fusion, for the improvement of the conflict in the routing protocol, and through machine learning Q algorithm to optimize the real-time transmission model, etc., ignore the different degree of preference for different information. With the help of this kind of information, the routing protocol can be optimized, and the complexity of the routing protocol can be further reduced. Improve the efficiency of Internet of Vehicles communication.

A transmission design in dynamic heterogeneous V2V networks through multi-agent deep reinforcement learning

A transmission design in dynamic heterogeneous V2V networks through multi-agent deep reinforcement learning

A Fast and Secured Vehicle-to-Vehicle Energy Trading Based on Blockchain Consensus in the Internet of Electric Vehicles

The organization and management of electricity markets worldwide are rapidly evolving, moving towards decentralized, distributed, and renewable energy-based generation with solutions based on real-time data exchange. A Vehicle-to-Vehicle (V2V) energy trading has emerged as one of the most promising alternatives for relieving the load imposed on the traditional grid enabling two individuals to buy and sell energy directly without intermediaries. However, the Internet of Electric Vehicles (IoEV) environment is trustless, and such P2P energy trading is prone to different kinds of cyber attacks. Blockchain technology has lately been proposed to implement V2V energy trading to securely and fairly share energy. The consensus mechanism is one of the most important modules of blockchain applied to the V2V network. It determines the efficiency and security among untrustworthy EVs of the energy trading blockchain (ETB). Nevertheless, most works on ETB have currently adopted traditional consensus mechanisms. Due to high computing power and communication overhead, these consensus algorithms are unsuitable for applications requiring real-time services such as energy trading. The efficient and secured Hashgraph is the revolutionary technology of consensus in blockchain and a promising technology suitable for V2V energy trading with frequent transactions. However, Hashgraph does not support the dynamic addition and deletion of nodes and is completely decentralized and vulnerable to Sybil Attack in a large-scale blockchain. Furthermore, this “complete decentralization” model may result in states losing the ability to macro-control the energy industry and even systemic energy security issues. Therefore, we propose a Block Alliance Consensus (BAC) mechanism to solve these problems. BAC can maintain the throughput of Hashgraph and resist Sybil Attack in a large-scale P2P energy trading network. We design a cryptography-based leader election mechanism and adopt a reputation incentive mechanism to motivate honest and cooperative electric vehicles (EVs). Finally, we implement ETB and the BAC consensus mechanism on the Hyperledger Fabric platform. The high efficiency and security of BAC and the blockchain-based V2V energy trading platform are verified through experiments.

Performance Analysis of a Reconfigurable-Intelligent-Surfaces-Assisted V2V Communication System

Novel reconfigurable Intelligent Surfaces (RISs) area technology can improve the communication performance by changing the wireless transmission environment. Introducing RIS technology into Vehicle-to-Vehicle (V2V) communication environments can enhance the communication reliability by creating Line-of-Sight (LoS) communication links, thereby effectively improving the communication performance. However, in RIS-assisted V2V large-scale communication networks, the stochasticity of network nodes and random interference can impact performance. In this article, we examine the outage communication transmission performance of an RIS-assisted V2V communication network. We select the signal transmission mode based on the obstacle presence between vehicles and use the stochastic geometry theory to calculate the probabilities of the two modes: direct mode and RIS-assisted mode. By deriving the communication distance distribution and the aggregate interference distribution, we evaluate V2V communication in the direct mode to assess its transmission performance in two scenarios and obtain the overall outage probability. The numerical results demonstrate a better performance in RIS-assisted V2V networks, with an improved optimal phase shift scheme over that of the original V2V network. Monte Carlo simulation validated our analytical findings.

V2V Network for Autonomous Vehicles

Autonomous vehicles are still an imperfect technology, and they still have certain situations where they do not know what to do. To solve this problem, or at least make this problem better, we are proposing a Vehicle to Vehicle Network. This paper will describe every aspect of the network.

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications.

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

Analytical Study of an IOT-based Accident Detection and Information Management System

Accidents wreak havoc on victims, costing them valuable time and money. After thorough investigation, it has been shown that the majority of accidents result in fatalities as a result of poor communication with the relevant medical authorities and the ensuing dearth of prompt medical attention. Several sensor nodes are combined in a single system to forecast likely accident combinations. Lab view-based simulation was used to handlepossible conditions for an accident to happen. With the IoT Interface, theproposed design would enable a novel model in the vehicular communication system to recognize various accident situations and provide associated information to the needy. The proposed model would handle all potential combinations and comparative analyses from low to high end cars, as well as provide a strategy framework for future IoT enabled v2v communication networks.

Self-adaptive anti-misalignment model for transceivers in hybrid (RF/VLC)-V2V network

In this paper, a self-adaptive anti-misalignment model for transceivers in hybrid radio frequency (RF) communication and visible light communication (VLC) vehicle-to-vehicle (V2V) network is firstly proposed to solve the transceiver misalignment (TM) between vehicles in adjacent lanes caused by vehicle states' changes. By the information on roads and the state of vehicles, traffic scenarios are divided into three categories: the static traffic scenario, the low-speed traffic scenario and the high-speed traffic scenario. By the vehicle behavior characteristics, the relationship between TMs and vehicle states in different traffic scenarios is established. By the relationship, the self-adaptive anti-misalignment model for transceivers is constructed. By the model, the TM can be predicted and the communication mode can be selected. By simulation, the effectiveness of the model is demonstrated. The simulation results show that the model has comparative advantages of preventing the VLC links' interruption and reducing communication modes’ handover numbers in different traffic scenarios.

One-bit signaling-based interference management for MIMO V2V sidelink

Massive multiple-input multiple-output can significantly increase the system capacity by allowing vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) networks to share the radio resource pool, if the inter-network interference is constrained. However, in order to control the inter-network interference, it is necessary to exchange channel state information (CSI) between the V2I and V2V networks, which requires a huge amount of signaling resources. This paper proposes an one-bit signaling-based interference management scheme to reduce the signaling overhead while controlling the interference from the V2V network to the V2I network.

VABLOCK: A blockchain-based secure communication in V2V network using icn network support technology

Vehicular Ad hoc Network (VANET) provides efficient communication among vehicles (V2V). The communication among all the vehicles complies with the on-demand, which contains a secure and trustable mechanism to ensure trustable communication. The modification in the communication information may result in falsified information. Secure data is very important in V2V communication to save the lives of pedestrians and drives by delivering secure and accurate information. To address the problem and achieve secure communication in V2V, we proposed a new blockchain-based message dissemination technique to secure V2V communication. With the passive need for adaptable and sufficient content delivery, information-centric networking (ICN) is adopted to enhance trustworthiness communication in VANET. We used Cluster-based secure communication through ICN-based VANETs. As VANET is open, ICN provides direct content requests and responses without location dependency. ICN-based VANET enhances caching abilities. The blockchain-based security protocol is implemented to secure efficient communication without altering the messages. The protocol achieves privacy, security, and trust for detecting malicious nodes in VANET. Additionally, the Clustering technique is applied to adopt in-range communication. Proposed-Caching framework enhances security and provides on-demand data to vehicles. NS-2 simulator is used to simulate the Proposed VABLOCK approach. Experimental results are performed and compared with relevant techniques, which show enhanced results based on cache hit ratio, one hop count, malicious node detection, and delivery ratio. We demonstrate that proposed caching improves results on selected parameters based on results.

Millimeter Wave Reflection Pattern Codebook Design for RIS-Assisted V2V Communications

We consider a reflection pattern (RP) designfor reconfigurable intelligent surface (RIS)-assisted vehicle-to-vehicle (V2V) communication systems. Fast-moving-vehicle environments necessitate developing more intuitive RP alignment technique that aligns a predefined codeword to the dominant channel directions. To support a fast RP alignment in RIS-assisted V2V systems, we provide a design guideline of the RP codebook by considering passive reflecting elements at the RIS. We analyze distributions of the incident and reflection channels by taking the street geometry in V2V networks into consideration. By using the statistical information on the difference in spatial frequencies between the incident and reflection channels, we define a set of ideal RPs that are likely to maximize a reflection beamforming gain. The RP codebook is constructed by designing a set of reflecting codewords that can generate actual RPs close to predefined ideal RPs. Numerical results verify that the proposed RP codebook provides a higher link gain than previously reported codebooks.

Blockchain-Based Dashcam Video Management Method for Data Sharing and Integrity in V2V Network

A dashboard camera (Dashcam) is attached to the front or rear of a vehicle to record images and video. As it is effective in crime prevention and accident management and it can also be used as learning data for Traffic Accident Detection technology for Autonomous Vehicles the use of Dashcam is increasing. However, the video data is stored on a memory card or a cloud server, so there is a high possibility of data loss and forgery. Although it is possible to prevent forgery and falsification using distributed storage based on blockchain technology, the authenticity and privacy of the image data stored in the blockchain cannot be guaranteed. In this study, to solve this problem, we propose a multi-signature-based access control method by grouping and storing video data of multiple vehicles based on GPS (Global Positioning System) data. To ensure the privacy of the video data stored in the blockchain, only users uploading video belonging to the relevant GPS can access nearby Dashcam videos. Through these experimental results, it show that Experimental results proposed method can maintain low latency in large-scale request environment to the privacy and reach data management efficiently in a distributed file system. According to our experiments, the dashcam video data distributed storage latency was fast enough, with an average of 25 ms for uploads and less than 15 ms for downloads. The signature generation time is 10ms, and the verification time required for access control is also less than 100 ms, which is the same even if the number of nodes increases, so scalability is not affected. The blockchain transaction processing took about 2 seconds, but it does not affect the V2V network. Also, client registration time does not affect performance.