Pub Date : 2024-11-15DOI: 10.1016/j.vehcom.2024.100858
Indu Chandran, Kizheppatt Vipin
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.
{"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}
Pub Date : 2024-11-08DOI: 10.1016/j.vehcom.2024.100854
Xiaoluoteng Song , Xiuwen Fu , Mingyuan Ren , Pasquale Pace , Gianluca Aloi , Giancarlo Fortino
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.
{"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}
Pub Date : 2024-11-07DOI: 10.1016/j.vehcom.2024.100850
Huizhi Tang, Abdul Rauf, Qin Lin, Guoqing Dou, Changshuai Qin
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.
{"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}
Pub Date : 2024-11-06DOI: 10.1016/j.vehcom.2024.100853
Haitao Li, Jiawei Huang
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.
{"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}
Pub Date : 2024-10-31DOI: 10.1016/j.vehcom.2024.100849
Chen Cheng, Linbo Zhai, Yujuan Jia, Xiumin Zhu, Yumei Li
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.
{"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}
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 2N 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 , which is the function of number of reconfigurable elements (N) 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.
本文深入探讨了通过同时传输和反射可重构智能表面（STAR-RIS）实现的非正交多址（NOMA）车对车（V2V）通信系统的性能评估。在此，我们考虑由一个移动接入点（AP）通过配备有 2N 个可重构元件的 STAR-RIS 的车辆，分别通过反射和传输向附近和远处的 NOMA 车辆同时传输叠加信号。具体地说，通过将所有 V2V 信道描述为双瑞利衰落分布，我们得出了每个 NOMA 车辆的中断概率（OP）和遍历容量（EC）表达式，并在附近车辆用户处采用了完美和不完美的连续干扰消除（SIC）。此外，我们还提出了高信噪比（SNR）情况下的渐进 OP 行为，以深入了解 NOMA 车辆的分集顺序。研究结果表明，在完美 SIC 条件下，近车和远车的分集阶为 Nπ4256-π2，这是 STAR-RIS 中可重构元素数量（N）的函数。而在不完善 SIC 的情况下，附近用户的分集阶数为零。此外，我们还分析讨论了两个用户车辆的高信噪比 EC 斜坡。此外，我们还进行了蒙特卡洛模拟，以验证我们在各种信道和系统参数配置下的分析结果。我们还对所提出的方案与基于 STAR-RIS 的正交多址和合作中继系统进行了比较。
{"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}
Pub Date : 2024-10-28DOI: 10.1016/j.vehcom.2024.100851
Tao Wang , Xiaodong Ji , Xuan Zhu , Cheng He , Jian-Feng Gu
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 of data. Moreover, it is more practical and efficient in terms of energy saving compared with the Deep Q Network based design.
{"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}
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.
{"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}
Pub Date : 2024-10-24DOI: 10.1016/j.vehcom.2024.100848
Junfeng Tian , Yue Shen , Yiting Wang
Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.
{"title":"Blockchain-based fast handover authentication protocol for Internet of Vehicles in small industrial parks","authors":"Junfeng Tian , Yue Shen , Yiting Wang","doi":"10.1016/j.vehcom.2024.100848","DOIUrl":"10.1016/j.vehcom.2024.100848","url":null,"abstract":"<div><div>Autonomous vehicles safeguard the security and efficiency of Internet of Vehicles systems in small industrial parks by authenticating and exchanging real-time information with transportation infrastructure. Deploying a multi-server framework reduces the risk of message blocking and privacy information leakage from centralized services. However, in traditional handover authentication protocols, there are still potential security risks such as high-overhead performance issues and single point of failure attacks. Therefore, it is considered challenging to realize efficient authentication while protecting the privacy of vehicles. In this paper, we propose a secure and efficient handover authentication protocol for autonomous vehicles in a small industrial park to address the challenges. The protocol is based on blockchain and Pedersen verifiable secret sharing scheme, which not only ensures lightweight real-time interactions between autonomous vehicles and edge servers in multi-server environments, but also strictly protects the security and privacy of both vehicles and edge servers. We prove the semantic security of the protocol under the Real-Or-Random model and perform a informal analysis of its security attributes to show that it can withstand a wide range of malicious attacks. Performance evaluation shows that the proposed protocol satisfies more security requirements and has better computational efficiency and communication cost than other related protocols.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100848"},"PeriodicalIF":5.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528445","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}
Pub Date : 2024-09-30DOI: 10.1016/j.vehcom.2024.100846
Batuhan Gul, Fatih Ertam
The utilization of autonomous vehicles is experiencing a rapid proliferation in contemporary society. Concurrently, with the relentless evolution of technology, the inexorable integration of autonomous vehicles into urban environments, driven by the overarching paradigm of smart cities, becomes increasingly apparent. This escalating reliance on autonomous vehicles concurrently heightens the susceptibility to malevolent actors orchestrating cyber-attacks against these vehicular systems. While previous years have seen a limited corpus of academic research pertaining to cyber-attack and defense methodologies for autonomous vehicles, the relentless progression of technology mandates a more contemporary and exhaustive inquiry. In addition, to the best of our knowledge, there is no article in the literature that provides detailed information and comparisons about in-vehicle sensors, in-vehicle networks, and in-vehicle network datasets by combining them in one article. Also, to our knowledge, very limited studies have been conducted on separately comparative analysis of in-vehicle networks, in-vehicle sensors or data sets in 2024, and therefore, the necessity of conducting a review study on these topics was recognized. To address this deficiency, we compile articles on attacks and defenses on sensors, in-vehicle networks and present detailed information about the latest datasets and provide comparative analysis. In this paper, we have analyzed 108 papers from the last 10 years on in-vehicle networks and sensors. 38 articles on in-vehicle sensors and 70 articles on in-vehicle networks were reviewed and analyzed. We categorize in-vehicle communication attacks into two main groups: sensor-initiated and network-initiated, with a chronological classification to highlight their evolution. We also compare the progress in securing in-vehicle communication and evaluate the most widely used datasets for attack and protection methods. Additionally, we discuss the advantages and disadvantages of these datasets and suggest future research directions. To the best of our knowledge, this work is the first to offer detailed information and comparative analysis of in-vehicle networks, sensors, and the latest datasets. While the study highlights the significant research conducted to protect in-vehicle networks and sensors from cyber attacks, technological advancements continue to introduce new attack vectors. Cars remain particularly susceptible to threats such as DoS, Fuzzy, Spoofing, and Replay attacks. Moreover, current defense mechanisms, including LSTM and CNN, have notable limitations. Future research is needed to address these challenges and enhance vehicle cybersecurity.
{"title":"In-vehicle communication cyber security: A comprehensive review of challenges and solutions","authors":"Batuhan Gul, Fatih Ertam","doi":"10.1016/j.vehcom.2024.100846","DOIUrl":"10.1016/j.vehcom.2024.100846","url":null,"abstract":"<div><div>The utilization of autonomous vehicles is experiencing a rapid proliferation in contemporary society. Concurrently, with the relentless evolution of technology, the inexorable integration of autonomous vehicles into urban environments, driven by the overarching paradigm of smart cities, becomes increasingly apparent. This escalating reliance on autonomous vehicles concurrently heightens the susceptibility to malevolent actors orchestrating cyber-attacks against these vehicular systems. While previous years have seen a limited corpus of academic research pertaining to cyber-attack and defense methodologies for autonomous vehicles, the relentless progression of technology mandates a more contemporary and exhaustive inquiry. In addition, to the best of our knowledge, there is no article in the literature that provides detailed information and comparisons about in-vehicle sensors, in-vehicle networks, and in-vehicle network datasets by combining them in one article. Also, to our knowledge, very limited studies have been conducted on separately comparative analysis of in-vehicle networks, in-vehicle sensors or data sets in 2024, and therefore, the necessity of conducting a review study on these topics was recognized. To address this deficiency, we compile articles on attacks and defenses on sensors, in-vehicle networks and present detailed information about the latest datasets and provide comparative analysis. In this paper, we have analyzed 108 papers from the last 10 years on in-vehicle networks and sensors. 38 articles on in-vehicle sensors and 70 articles on in-vehicle networks were reviewed and analyzed. We categorize in-vehicle communication attacks into two main groups: sensor-initiated and network-initiated, with a chronological classification to highlight their evolution. We also compare the progress in securing in-vehicle communication and evaluate the most widely used datasets for attack and protection methods. Additionally, we discuss the advantages and disadvantages of these datasets and suggest future research directions. To the best of our knowledge, this work is the first to offer detailed information and comparative analysis of in-vehicle networks, sensors, and the latest datasets. While the study highlights the significant research conducted to protect in-vehicle networks and sensors from cyber attacks, technological advancements continue to introduce new attack vectors. Cars remain particularly susceptible to threats such as DoS, Fuzzy, Spoofing, and Replay attacks. Moreover, current defense mechanisms, including LSTM and CNN, have notable limitations. Future research is needed to address these challenges and enhance vehicle cybersecurity.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100846"},"PeriodicalIF":5.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421182","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}
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