Vehicular Communications最新文献_第7页

Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization 基于异构代理近端策略优化的资源分配算法促进车辆互联互通

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-15 DOI: 10.1016/j.vehcom.2024.100856

Junhui Zhao , Xincheng Xiong , Qingmiao Zhang , Shihai Ren , Jingyan Chen , Wei Xu , Dongming Wang

{"title":"Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization","authors":"Junhui Zhao , Xincheng Xiong , Qingmiao Zhang , Shihai Ren , Jingyan Chen , Wei Xu , Dongming Wang","doi":"10.1016/j.vehcom.2024.100856","DOIUrl":"10.1016/j.vehcom.2024.100856","url":null,"abstract":"<div><div>Vehicle-to-Vehicle (V2V) communication can not only provide unrestricted inter-vehicle information transmission, but also improve spectrum utilization efficiency. However, it also brings uncontrollable co-channel interference, which can not guarantee the quality of service of V2V communication. In this paper, we propose an intelligent resource allocation scheme for V2V communication to improve vehicle connectivity. To enhance cooperation among vehicles and avoid excessive co-channel interference between them, we propose an asynchronous resource allocation method where vehicles choose to send or not to send data based on observed environmental information to ensure stable overall performance. Furthermore, we present a novel resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization (HAPPO) to solve the resource allocation problem in asynchronous vehicular networks. The HAPPO algorithm calculates the global advantage function when each agent makes an action during the training process to ensure that the action taken contributes to the overall performance improvement. Our proposed approach improves the robustness of V2V communication by reducing co-channel interference while maintaining stable overall performance. Simulation results show that the proposed approach can effectively improve the V2V communication connectivity and reduce the packet loss rate compared with the existing methods.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100856"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized multi-hop data processing in UAV networks using MARL 利用 MARL 在无人机网络中进行分散式多跳数据处理

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-15 DOI: 10.1016/j.vehcom.2024.100858

Indu Chandran, Kizheppatt Vipin

{"title":"Decentralized multi-hop data processing in UAV networks using MARL","authors":"Indu Chandran, Kizheppatt Vipin","doi":"10.1016/j.vehcom.2024.100858","DOIUrl":"10.1016/j.vehcom.2024.100858","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have become integral to numerous applications, prompting research towards enhancing their capabilities. For time-critical missions, minimizing latency is crucial; however, current studies often rely on sending data to ground station or cloud for processing due to their limited onboard capacities. To leverage the networking capabilities of UAVs, recent research focuses on enabling data processing and offloading within the UAV network for coordinated decision-making. This paper explores a multi-hop data offloading scheme designed to optimize the task processing and resource management of UAVs. The proposed distributed strategy uses multi-agent reinforcement learning, where UAVs, each with varying computational capacities and energy levels, process and offload tasks while managing energy consumption and latency. The agents, represented as actor-critic models, learn and adapt their actions based on current state and environment feedback. The study considers a consensus-based method to update learning weights, promoting cooperative behavior among the agents with minimum interaction. Through multiple training episodes, the agents improve their performance, with the overall system achieving faster convergence with high rewards, demonstrating the viability of decentralized data processing and offloading in UAV networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100858"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of electric vehicle charging and scheduling based on VANETs 基于 VANET 的电动汽车充电和调度优化

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-15 DOI: 10.1016/j.vehcom.2024.100857

Tianyu Sun , Ben-Guo He , Junxin Chen , Haiyan Lu , Bo Fang , Yicong Zhou

{"title":"Optimization of electric vehicle charging and scheduling based on VANETs","authors":"Tianyu Sun , Ben-Guo He , Junxin Chen , Haiyan Lu , Bo Fang , Yicong Zhou","doi":"10.1016/j.vehcom.2024.100857","DOIUrl":"10.1016/j.vehcom.2024.100857","url":null,"abstract":"<div><div>Vehicular Ad-hoc Networks (VANETs) provide key support for the achievement of intelligent, safe, and efficient driverless transportation systems through real-time communication between vehicles and vehicles, and vehicles and road infrastructure. This paper investigates a joint optimization problem of electric vehicles (EVs) charging management and resource allocation based on VANETs. EV charging requires significantly more time than refueling conventional vehicles, a key factor behind people's reluctance to transition from internal combustion engine vehicles to EVs. Previous works have primarily concentrated on fully-charged vehicles and random matching, which does not solve the problems of vehicle charging delays and long customer waiting times. Considering these factors, we propose a distributed multi-level charging strategy and level-by-level matching method. Specifically, EVs and passengers are categorized into classes based on battery power and target mileage. Vehicles are then allocated to customers in the same or lower levels. Furthermore, the Attentive Temporal Convolutional Networks-Long Short Term Memory (ATCN-LSTM) model is leveraged to predict historical traffic data, supporting anticipatory decision-making. Subsequently, we develop a hierarchical charging and rebalancing joint optimization framework that incorporates charging facility planning. Experimental results obtained under various model parameters exhibit the method's commendable performance, as evidenced by metrics such as operating cost, system response time, and vehicle utilization.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100857"},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction-based data collection of UAV-assisted Maritime Internet of Things 基于预测的无人机辅助海上物联网数据收集

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-08 DOI: 10.1016/j.vehcom.2024.100854

Xiaoluoteng Song , Xiuwen Fu , Mingyuan Ren , Pasquale Pace , Gianluca Aloi , Giancarlo Fortino

{"title":"Prediction-based data collection of UAV-assisted Maritime Internet of Things","authors":"Xiaoluoteng Song , Xiuwen Fu , Mingyuan Ren , Pasquale Pace , Gianluca Aloi , Giancarlo Fortino","doi":"10.1016/j.vehcom.2024.100854","DOIUrl":"10.1016/j.vehcom.2024.100854","url":null,"abstract":"<div><div>In maritime data collection scenarios, due to the constraints of wireless communication and environmental factors such as wave motion, sea surface ducting effects, and sea surface curvature, floating sensor nodes are unable to establish direct data transmission links with the base station. The advent of unmanned aerial vehicle (UAV)-assisted Maritime Internet of Things (MIoT) provides a feasible solution to this challenge. However, in existing maritime environments, floating sensor nodes drift due to ocean currents, posing significant challenges for long-distance data transmission while maintaining a low age of information (AoI). Consequently, we introduce a prediction-based UAV-assisted data collection mechanism for MIoT. In this scheme, we first select convergence nodes responsible for gathering data from floating sensor nodes and forwarding it to passing UAVs. We then propose a dynamic clustering algorithm to allocate task areas to UAVs, with each area assigned to a single UAV for data collection from floating sensor nodes. To ensure stable data offloading by UAVs, we develop a UAV relay pairing algorithm to establish reliable air-to-air relay paths and provide two data offloading modes: distal UAV and proximate UAV. Owing to the drift of floating sensor nodes influenced by ocean currents, we employ a deep echo state network to predict the positions of floating sensor nodes and utilize a multi-agent deep deterministic policy gradient to solve the UAVs trajectory planning problem. Under this mechanism, the UAVs can adaptively adjust its flight path while exploring floating sensor nodes in dynamically changing ocean sensor node scenarios. Extensive experiments demonstrate that the proposed scheme can adapt to dynamic ocean environments, achieving low-AoI data collection from floating sensor nodes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100854"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid mutual authentication for vehicle-to-infrastructure communication without the coverage of roadside units 在没有路边装置覆盖的情况下，实现车辆与基础设施通信的混合相互认证

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-07 DOI: 10.1016/j.vehcom.2024.100850

Huizhi Tang, Abdul Rauf, Qin Lin, Guoqing Dou, Changshuai Qin

{"title":"Hybrid mutual authentication for vehicle-to-infrastructure communication without the coverage of roadside units","authors":"Huizhi Tang, Abdul Rauf, Qin Lin, Guoqing Dou, Changshuai Qin","doi":"10.1016/j.vehcom.2024.100850","DOIUrl":"10.1016/j.vehcom.2024.100850","url":null,"abstract":"<div><div>The security issues in Vehicle Ad Hoc Networks (VANETs) are prevalent within Intelligent Transportation Systems (ITS). To ensure the security of vehicle-to-infrastructure (V2I) communication, extensive research on V2I authentication has been conducted in recent years. However, these protocols often overlook the limitations of communication range, leading to failures in V2I communication. Consequently, addressing the challenge of secure V2I communication in areas not covered by distributed roadside units (RSUs) remains a significant task. To address these issues, the current study proposes an Anonymous Certificate-less Hybrid Mutual Authentication Protocol (ACHMAP) based on Vehicle-to-Vehicle-to-Infrastructure (V2V2I) communication. In the proposed protocol, a secure multi-hop link is established through vehicle-to-vehicle (V2V) mutual one-time token authentication. Subsequently, the out-of-coverage vehicle and relevant RSUs complete V2I mutual authentication using signcryption messages transmitted by vehicle nodes. In the security analysis, it is demonstrated that the entire V2V2I stage can resist various security attacks, such as replay attacks, impersonation attacks, and threats to user anonymity, while preserving confidentiality and integrity. We simulated the proposed protocol using Network Simulator 3 (NS-3) to confirm that the authentication mechanism has lower overhead and minimal authentication delay in V2V2I communication.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100850"},"PeriodicalIF":5.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness 基于分层联合深度强化学习的无人机态势感知联合通信与计算

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-11-06 DOI: 10.1016/j.vehcom.2024.100853

Haitao Li, Jiawei Huang

{"title":"Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness","authors":"Haitao Li, Jiawei Huang","doi":"10.1016/j.vehcom.2024.100853","DOIUrl":"10.1016/j.vehcom.2024.100853","url":null,"abstract":"<div><div>The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100853"},"PeriodicalIF":5.8,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Volunteer vehicle assisted dependent task offloading based on ant colony optimization algorithm in vehicular edge computing 车载边缘计算中基于蚁群优化算法的志愿车辆辅助依赖任务卸载

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-10-31 DOI: 10.1016/j.vehcom.2024.100849

Chen Cheng, Linbo Zhai, Yujuan Jia, Xiumin Zhu, Yumei Li

{"title":"Volunteer vehicle assisted dependent task offloading based on ant colony optimization algorithm in vehicular edge computing","authors":"Chen Cheng, Linbo Zhai, Yujuan Jia, Xiumin Zhu, Yumei Li","doi":"10.1016/j.vehcom.2024.100849","DOIUrl":"10.1016/j.vehcom.2024.100849","url":null,"abstract":"<div><div>Vehicle Edge Computing improves the Quality of Service of vehicular applications by offloading tasks to the VEC server. However, with the continuous development of computation-intensive vehicular applications, the limited resources of the VEC server will not be enough to support these applications. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) proposes a concept of using vehicles as resources, which is considered to be a promising solution. In this paper, we study the multi-dependent task offloading problem in order to quickly and economically handle the overload task of the requesting vehicle in VCBV. Considering both task execution delay and execution cost, we formulate the problem of offloading the multi-dependent tasks of requesting vehicles to minimize total task completion time and execution cost. Since the offloading problem is NP-hard, an improved multi-objective Ant Colony Optimization algorithm is proposed. Firstly, we use a density-based clustering algorithm to form volunteer alliances that can contribute idle resources. Secondly, based on the volunteer alliances and RSUs, we use Analytic Hierarchy Process (AHP) to initialize pheromone concentration to make better decisions. Then, we design the update strategy of the pheromone concentration and heuristic information. Finally, we introduce Pareto optimal relationship to evaluate the results. A large number of simulation results verify that our algorithm has better performance than other alternatives.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100849"},"PeriodicalIF":5.8,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STAR-RIS-NOMA empowered vehicle-to-vehicle communications: Outage and ergodic capacity analysis STAR-RIS-NOMA 赋权车对车通信：中断和遍历容量分析

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-10-28 DOI: 10.1016/j.vehcom.2024.100852

Luxmi Kant Vishwakarma , Radhika Gour , Suneel Yadav , Adão Silva

{"title":"STAR-RIS-NOMA empowered vehicle-to-vehicle communications: Outage and ergodic capacity analysis","authors":"Luxmi Kant Vishwakarma , Radhika Gour , Suneel Yadav , Adão Silva","doi":"10.1016/j.vehcom.2024.100852","DOIUrl":"10.1016/j.vehcom.2024.100852","url":null,"abstract":"<div><div>This paper delves into the performance evaluation of a non-orthogonal multiple access (NOMA) enabled vehicle-to-vehicle (V2V) communication system empowered by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). Herein, we consider that a moving access point (AP) transmits superimposed signals to nearby and distant NOMA vehicles simultaneously via reflection and transmission through a STAR-RIS equipped vehicle with 2<em>N</em> reconfigurable elements, respectively. Specifically, by characterizing all V2V channels as double-Rayleigh fading distributed, we derive the outage probability (OP) and ergodic capacity (EC) expressions for each NOMA vehicle, by employing both perfect and imperfect successive interference cancellation (SIC) at nearby vehicle user. Furthermore, we present the asymptotic OP behavior at high signal-to-noise ratio (SNR) regime to gain deeper insights into the diversity order of NOMA vehicles. The findings reveal that the nearby vehicle under perfect SIC and far vehicle experience a diversity order of <span><math><mfrac><mrow><mi>N</mi><msup><mrow><mi>π</mi></mrow><mrow><mn>4</mn></mrow></msup></mrow><mrow><mn>256</mn><mo>−</mo><msup><mrow><mi>π</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></mfrac></math></span>, which is the function of number of reconfigurable elements (<em>N</em>) in the STAR-RIS. Whereas, a zero diversity order is obtained for nearby user under imperfect SIC case. Moreover, we analytically discuss the high SNR slopes of EC for both user vehicles. Furthermore, Monte-Carlo simulations are conducted to validate our analytical results under various channel and system parameter configurations. We also provide a comparison between the proposed scheme and STAR-RIS based orthogonal multiple access and cooperative relaying systems.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100852"},"PeriodicalIF":5.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network 基于深度强化学习的固定翼无人机辅助移动中继网络运行轨迹路径设计

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-10-28 DOI: 10.1016/j.vehcom.2024.100851

Tao Wang , Xiaodong Ji , Xuan Zhu , Cheng He , Jian-Feng Gu

{"title":"Deep Reinforcement Learning based running-track path design for fixed-wing UAV assisted mobile relaying network","authors":"Tao Wang , Xiaodong Ji , Xuan Zhu , Cheng He , Jian-Feng Gu","doi":"10.1016/j.vehcom.2024.100851","DOIUrl":"10.1016/j.vehcom.2024.100851","url":null,"abstract":"<div><div>This paper studies a fixed-wing unmanned aerial vehicle (UAV) assisted mobile relaying network (FUAVMRN), where a fixed-wing UAV employs an out-band full-duplex relaying fashion to serve a ground source-destination pair. It is confirmed that for a FUAVMRN, straight path is not suitable for the case that a huge amount of data need to be delivered, while circular path may lead to low throughput if the distance of ground source-destination pair is large. Thus, a running-track path (RTP) design problem is investigated for the FUAVMRN with the goal of energy minimization. By dividing an RTP into two straight and two semicircular paths, the total energy consumption of the UAV and the total amount of data transferred from the ground source to the ground destination via the UAV relay are calculated. According to the framework of Deep Reinforcement Learning and taking the UAV's roll-angle limit into consideration, the RTP design problem is formulated as a Markov Decision Process problem, giving the state and action spaces in addition to the policy and reward functions. In order for the UAV relay to obtain the control policy, Deep Deterministic Policy Gradient (DDPG) is used to solve the path design problem, leading to a DDPG based algorithm for the RTP design. Computer simulations are performed and the results show that the DDPG based algorithm always converges when the number of training iterations is around 500, and compared with the circular and straight paths, the proposed RTP design can save at least 12.13 % of energy and 65.93 % of flight time when the ground source and the ground destination are located 2000 m apart and need to transfer <span><math><mrow><mn>5000</mn><mrow><mtext>bit</mtext><mo>/</mo><mtext>Hz</mtext></mrow></mrow></math></span> of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100851"},"PeriodicalIF":5.8,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET EPAKA：基于物理安全的高效且保护隐私的 VANET 验证密钥协议方案

IF 5.8 2区计算机科学

Vehicular Communications Pub Date : 2024-10-24 DOI: 10.1016/j.vehcom.2024.100847

Chunhua Jin , Penghui Zhou , Zhiwei Chen , Wenyu Qin , Guanhua Chen , Hao Zhang , Jian Weng

{"title":"EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET","authors":"Chunhua Jin , Penghui Zhou , Zhiwei Chen , Wenyu Qin , Guanhua Chen , Hao Zhang , Jian Weng","doi":"10.1016/j.vehcom.2024.100847","DOIUrl":"10.1016/j.vehcom.2024.100847","url":null,"abstract":"<div><div>Vehicular ad hoc network (VANET) has been a promising technology in smart transportation system, which can enable information exchange between vehicles and roadside units (RSUs). However, the privacy of vehicles and RSUs is a critical challenge in VANET, as they may expose sensitive information to malicious attackers or unauthorized parties. Many existing authenticated key agreement (AKA) schemes aim to protect the privacy of vehicles and RSUs, but they often neglect the physical security of the devices involved in the communication. Therefore, we propose an efficient and privacy-preserving AKA scheme in VANET, which embeds physical unclonable function (PUF) and fuzzy extraction (FE) technology. PUF is a physical device that generates random strings based on their intrinsic characteristics and external inputs, which can protect the secrets in the devices from being stolen by attackers. FE can compensate for the drawbacks of PUF affected by environmental factors. Our scheme preserves the identity privacy of legitimate RSUs and vehicles, as well as intercepts and traces the identity of malicious attackers. In addition, we eliminate the involvement of the third party (TP) in the AKA phase to better meet the high-speed driving of vehicles. Finally, we conduct formal and informal security analyses in random oracle model (ROM), which prove that our scheme can resist various attacks. We also show in the performance analysis that our scheme has the lowest computational cost, communication overhead, and total energy consumption.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100847"},"PeriodicalIF":5.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0