Vehicular Communications最新文献

{"title":"Quantum-resilient blockchain-enabled secure communication framework for connected autonomous vehicles using post-quantum cryptography","authors":"Anjum Mohd Aslam,&nbsp;Aditya Bhardwaj,&nbsp;Rajat Chaudhary","doi":"10.1016/j.vehcom.2025.100880","DOIUrl":"10.1016/j.vehcom.2025.100880","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) are pivotal to the evolution of Intelligent Transportation Systems (ITS), offering enhanced connectivity and automation. However, the emergence of quantum computing poses significant security challenges to existing cryptographic protocols. This study addresses these challenges by proposing a hybrid security approach that combines Kyber Post-Quantum Cryptography (PQC) with an Adaptive Grouping Score-based Practical Byzantine Fault Tolerance (AGS-PBFT) blockchain mechanism. The key contribution of this study is the integration of lattice-based Kyber PQC, which is resistant to quantum attacks, with a dynamically adaptive AGS-PBFT blockchain. This integration aims to secure vehicular communications by ensuring data integrity, authenticity, and confidentiality while enhancing the scalability and efficiency of consensus processes in dynamic CAVs environments.</div><div>The proposed hybrid approach has been validated through extensive simulations using the OMNET++ and SUMO simulators. The simulation results demonstrate that our approach outperforms existing methods, achieving lower latency, reduced computation costs, higher throughput, and enhanced security levels. Overall, the findings and methodologies presented in the paper can serve as a valuable reference for researchers and practitioners aiming to enhance the security and efficiency of CAVs in the era of quantum computing.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100880"},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049952","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}

{"title":"A task-driven scheme for forming clustering-structure-based heterogeneous FANETs","authors":"Siji Chen ,&nbsp;Bo Jiang ,&nbsp;Hong Xu ,&nbsp;Tao Pang ,&nbsp;Mingke Gao ,&nbsp;Ziyang Liu","doi":"10.1016/j.vehcom.2025.100884","DOIUrl":"10.1016/j.vehcom.2025.100884","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to be used in industries and various sectors of human life to provide a wide range of applications and services, significantly enhancing its applicability in different fields. When a UAV swarm performs complex tasks, flying Ad-hoc networks (FANETs) based on cluster structures have become a key research topic in the field of topology control due to their strong scalability and low routing overhead. However, current research mainly concentrates on the selection of the cluster head (CH), considering all UAVs within the CH's communication radius as cluster members (CMs), often neglecting whether the cluster can effectively accomplish the task, thereby potentially leading to mission failure. To overcome this problem, this paper innovatively proposes a task-driven clustering (TDC-MOPSO) algorithm based on improved multi-objective particle swarm optimization (MOPSO) for clustering-structure-based heterogeneous FANETs, which introduces the transfer function to improve the search range of particles and the mutation mechanism to avoid falling into local optima, and a more reasonable fitness function is designed to select CHs. The simulation results indicate that the proposed TDC-MOPSO algorithm dramatically improves the task completion rate by up to about 41.32% and extends the node lifetime by up to about 50.12% compared to traditional clustering algorithms. Meanwhile, the TDC-MOPSO algorithm improves the task completion rate by up to about 11.02% compared to other mopso-based algorithms. Furthermore, the TDC-MOPSO algorithm obtains more clustering solutions with higher average energy, less waste of resources, less CH handover rate, and less routing overhead in simulation. The proposed algorithm is also verified in a real-life scenario, which also effectively supports the completion of the task. All of which demonstrates that the TDC-MOPSO algorithm enhances the efficiency of task execution while ensuring communication performance for clustering-structure-based FANETs.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100884"},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990021","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}

{"title":"Fairness aware secure energy efficiency maximization for UAV-assisted data collection in backscattering networks","authors":"Jiawang Zeng,&nbsp;Deepak Mishra,&nbsp;Hassan Habibi Gharakheili,&nbsp;Aruna Seneviratne","doi":"10.1016/j.vehcom.2025.100881","DOIUrl":"10.1016/j.vehcom.2025.100881","url":null,"abstract":"<div><div>Autonomous vehicles for intelligent surveillance in rural areas increasingly demand low-cost and reliable data collection technologies to perform dense monitoring across extended areas. Backscattering communication has been employed for this purpose, primarily for low-cost and energy efficiency reasons. This paper considers a backscattering data collection system empowered by unmanned aerial vehicles (UAVs) to overcome the challenge of wireless coverage and provide backscattering tags with physical-layer security. Relevant prior works only focused on the secrecy of backscattering communications, while the limited battery of UAVs was overlooked during the underlying vehicle control. This paper aims to jointly optimize the trajectory of multiple UAVs and choice of tags, as well as tags' reflection parameters, to manage data leakage and total energy consumed by UAVs during a round of data collection. Our specific contributions are threefold. (1) We propose a 3D multi-UAV backscattering data collection framework and formulate an optimization problem to maximize the ratio of secrecy across all tags to the power consumption of UAVs subject to some practical constraints. (2) We show that our problem is non-convex and partition it into three sub-problems, transform objective functions, and relax certain constraints to obtain approximate convex problems that yield suboptimal solutions. (3) We evaluate the efficacy of our proposed intelligent security protocol for UAV-assisted data collection, compare its performance with some baseline schemes, our protocal achieve leading performance in terms of secrecy energy efficiency. We also provide the impact of parameters on the secrecy energy efficiency, as well as quantify its complexity via extensive simulations.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100881"},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An energy-efficient distributed computation offloading algorithm for ground-air cooperative networks","authors":"Yanling Shao ,&nbsp;Hairui Xu ,&nbsp;Liming Liu ,&nbsp;Wenyong Dong ,&nbsp;Pingping Shan ,&nbsp;Junying Guo ,&nbsp;Wenxuan Xu","doi":"10.1016/j.vehcom.2025.100875","DOIUrl":"10.1016/j.vehcom.2025.100875","url":null,"abstract":"<div><div>Due to the shortage of energy resources and computational capability, unmanned aerial vehicles (UAVs) tend to fail to execute tasks with time-delay sensitive and complex demands like artificial intelligence (AI) enabled applications. Most offloading method literature in ground-air cooperative systems simply uses edge servers or remote cloud servers to provide computation resources and storage space. Unfortunately, their performance degrades since it is difficult to guarantee UAV's quality of experience (QoE) considering the long-distance transmission delay. To address this issue, this paper proposes a ground-air cooperative edge computing framework in which multiprocessing computation is implemented by the UAVs locally or offloads specific calculations to the edge server on unmanned ground vehicles (UGVs). The proposed framework consists of two innovative mechanisms: one is to consider a mobility-aware link prediction method and other indicators, including compute capacity and workload, to ensure a stable offloading environment, the another is to propose an energy-efficient distributed computation offloading algorithm (EDCOA) by modelling the computation offloading issue for UAVs as an analytical optimization problem. By offloading subtasks to multiple UGV nodes for multiprocessing, UAVs can leverage the computation resources of the surrounding edge network entities to enhance their computational capabilities. Extensive experiments and comparisons with state-of-the-art realtime offloading methods showed that the proposed framework outperforms other approaches by delivering better performance in reducing UAV energy consumption, ensuring successful task offloading rates and meeting latency requirements.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100875"},"PeriodicalIF":5.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990026","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}

{"title":"Exploring V2X in 5G networks: A comprehensive survey of location-based services in hybrid scenarios","authors":"Bruno Mendes ,&nbsp;Marco Araújo ,&nbsp;Adriano Goes ,&nbsp;Daniel Corujo ,&nbsp;Arnaldo S.R. Oliveira","doi":"10.1016/j.vehcom.2025.100878","DOIUrl":"10.1016/j.vehcom.2025.100878","url":null,"abstract":"<div><div>Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as by country-specific spectrum regulations. The world's largest economies, such as the USA, EU and China have self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety and non-safety related V2X communication services are allowed to occur (always aligned with the aforementioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NR-U) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but since public spectrum is purchased through auctions, it is quite common the case that one particular operator will own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even location-based services, such as the ones that require the usage of positioning technologies, like autonomous vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the information is constrained to the individual vehicle and not shared with others in a secure manner. This exchange of position information between other entities (not only vehicles, but also other infrastructure nodes) is vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a comprehensive review connecting these diverse elements, examining the role of 5G private networks in transmitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying areas for further research.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"52 ","pages":"Article 100878"},"PeriodicalIF":5.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thank you reviewers","authors":"","doi":"10.1016/j.vehcom.2024.100872","DOIUrl":"10.1016/j.vehcom.2024.100872","url":null,"abstract":"","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100872"},"PeriodicalIF":5.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790083","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}

{"title":"Joint edge caching and computation offloading for heterogeneous tasks in MEC-enabled vehicular networks","authors":"Yangqianhang Li,&nbsp;Li Li,&nbsp;Zhaorong Zhou","doi":"10.1016/j.vehcom.2024.100860","DOIUrl":"10.1016/j.vehcom.2024.100860","url":null,"abstract":"<div><div>Edge caching is an effective paradigm that can significantly reduce the computation task offloading latency for mobile edge computing (MEC) in vehicular networks, while also alleviating the backhaul transmission pressure for retrieving content data from the cloud server. However, most existing works fail to address how to handle heterogeneous tasks generated by vehicle terminals (VTs), especially in complex scenarios where both computation and content tasks are generated simultaneously. In this paper, we consider a mobility-aware vehicular network model where VTs simultaneously generate heterogeneous task requests, i.e., a computation task and a content task, and investigate joint optimization of caching for heterogeneous tasks data, computation offloading, and computing resource allocation. In order to optimize the latency for processing the heterogeneous tasks, an average execution latency minimization problem with sojourn time and caching capacity constraints is formulated. We decompose this problem into two tractable subproblems, i.e., caching optimization subproblem, and computation offloading and resource allocation optimization subproblem. We first develop a dynamic programming (DP) algorithm to obtain the optimal caching strategies for heterogeneous tasks data. We compare the obtained content retrieval latency with the local computing latency, and derive the optimal computation offloading and edge computing resource allocation solutions. On this basis, we propose a joint computation offloading and resource allocation (JCORA) algorithm to determine the computing resources allocated to each VT and corresponding computation offloading strategy. Numerical results indicate that the proposed algorithm, which integrates DP algorithm and JCORA algorithm, can achieve lower execution latency for heterogeneous tasks compared to the benchmark schemes. Additionally, for task loss scenarios where the sojourn time constraint cannot be met, the impact of VT mobility on the task loss probability is also revealed.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100860"},"PeriodicalIF":5.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745715","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}

{"title":"Cooperative approach for data-centric and node-centric misbehavior detection in VANET","authors":"Rukhsar Sultana,&nbsp;Jyoti Grover,&nbsp;Meenakshi Tripathi","doi":"10.1016/j.vehcom.2024.100855","DOIUrl":"10.1016/j.vehcom.2024.100855","url":null,"abstract":"<div><div>Vehicular Ad Hoc Network (VANET) has risen as a paramount technology for efficiently providing traffic management, safety and infotainment services to road users. Vehicles are allowed to use pseudo identities during vehicular network access to preserve their privacy. This property makes VANET vulnerable to Sybil attack, performed by exploiting the set of pseudo identities to send messages. Detecting a Sybil attack solely by verifying the accuracy of messages received is challenging, as the messages sent through Sybil identities can appear plausible. Current data-centric and certain machine learning-based approaches only identify Sybil attacks within a local context. It is necessary to find the connection between the Sybil nodes both locally and at the Road Side Unit (RSU) level to effectively mitigate this attack. Hence, we introduce a novel cooperative and hybrid misbehavior detection framework for Sybil attack detection in VANET. It does not only detect Sybil identities but also establishes connections between them by analyzing their speed time series with the Dynamic Time Warping (DTW) technique. Furthermore, it confirms the association between Sybil nodes through node-centric detection using Dempster Shafer Theory (DST) at RSU. This advanced detection can help the Linkage Authority (LA) to find and revoke the actual node responsible for carrying out Sybil attack globally. This is the first framework in its category which can provide accurate detection at both local and RSU level in different scenarios. We acquired a higher detection rate by assessing performance with an existing dataset and a generated real-time Sybil attack dataset.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"50 ","pages":"Article 100855"},"PeriodicalIF":5.8,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696327","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}

{"title":"Boosting vehicular connectivity through resource allocation algorithm based on Heterogeneous Agent Proximal Policy Optimization","authors":"Junhui Zhao ,&nbsp;Xincheng Xiong ,&nbsp;Qingmiao Zhang ,&nbsp;Shihai Ren ,&nbsp;Jingyan Chen ,&nbsp;Wei Xu ,&nbsp;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}

{"title":"Decentralized multi-hop data processing in UAV networks using MARL","authors":"Indu Chandran,&nbsp;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}