Vehicular Communications最新文献

{"title":"Energy-harvesting relay-assisted STAR-RIS-enhanced vehicular NOMA networks","authors":"Jianghui Liu ,&nbsp;Saibing Wang ,&nbsp;Baofeng Ji ,&nbsp;Ruijuan Zheng ,&nbsp;Hao Li ,&nbsp;Guoqiang Zheng","doi":"10.1016/j.vehcom.2025.100960","DOIUrl":"10.1016/j.vehcom.2025.100960","url":null,"abstract":"<div><div>The integration of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) with non-orthogonal multiple access (NOMA) technology represents an effective approach for enabling massive device connectivity and achieving 360° network coverage. Under the NOMA scheme, the communication performance of weak users can be significantly enhanced, thereby improving user fairness; however, this often comes at the cost of performance degradation for strong users. Therefore, an energy harvesting relay-assisted STAR-RIS enhanced vehicular NOMA network is considered in this paper. Specifically, the paper provides a detailed analysis of the signal-to-noise ratio (SNR) at the near and far vehicles within the STAR-RIS-assisted vehicular NOMA network. The channel gain from the base station to the near vehicle via STAR-RIS and the composite channel gain between the base station and the far vehicle are approximated using Gamma distributions, with the accuracy of these approximations validated through Monte Carlo simulations. Based on the end-to-end SNR and the statistical characteristics of the channels, closed-form approximate expressions for the outage probabilities of near and far vehicles are rigorously derived, where the direct communication link exists between the far vehicle and the base station, while the energy-harvesting relay provides an auxiliary communication for the near vehicle. All analytical results are validated through simulations. The numerical and simulation results show that, without increasing the number of STAR-RIS elements, the outage performance of the near and far vehicles can be effectively controlled by adjusting the power allocation at the base station and the reflection/transmission coefficients of the STAR-RIS elements. This approach promotes fairness among vehicle users. Moreover, comparison with the orthogonal multiple access (OMA) scheme demonstrates that NOMA achieves better fairness between vehicle users and significantly reduces the outage probability for the far vehicle. Additionally, the energy-harvesting relay helps alleviate the negative impact of NOMA on the near vehicle, thereby further enhancing its communication stability.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100960"},"PeriodicalIF":5.8,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703541","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":"Fault tolerant mission management for UAV under random threat using Markov decision process","authors":"Md Muzakkir Quamar ,&nbsp;Ali Nasir ,&nbsp;Sami ELFerik","doi":"10.1016/j.vehcom.2025.100959","DOIUrl":"10.1016/j.vehcom.2025.100959","url":null,"abstract":"<div><div>This research introduces a comprehensive framework for mission accomplishment of unmanned aerial vehicles (UAVs) operating in threat-prone zones. Leveraging a Markov Decision Process (MDP), the proposed model ensures mission accomplishment and optimal resource utilization by incorporating UAV state-of-charge, post-fault capabilities, and threat navigation strategies. The framework addresses sensor, actuator, and vision camera faults, enabling dependable operations even under adverse conditions. A key feature of the model is the integration of UAV battery levels to evaluate operational range relative to mission objectives, optimizing task distribution while conserving energy. Additionally, the incorporation of adaptive navigation modes enhances UAV agility and robustness by enabling threat avoidance during operation, closely emulating real-world scenarios. By synthesizing repair protocols, recharging strategies, and stochastic modeling of recurrent goals and threats, the framework offers a holistic solution to improve resilience and mission success in hostile environments. Stochastic dynamic programming ensures the rapid application of precomputed optimal policies during mission execution. A simulation-based case study demonstrates the framework's effectiveness in navigating threats, mitigating faults, and ensuring mission reliability in energy-constrained scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100959"},"PeriodicalIF":5.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696823","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 secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANET","authors":"Dongxian Shi ,&nbsp;Xuwen Nie ,&nbsp;Ming Xu ,&nbsp;Hongbing Cheng ,&nbsp;Muhammad Alam","doi":"10.1016/j.vehcom.2025.100958","DOIUrl":"10.1016/j.vehcom.2025.100958","url":null,"abstract":"<div><div>On the benefit of the prosperous development of the communication techniques and automatic driving, the vehicle ad hoc network (VANET) is becoming more and more commonplace. The information transmitted in the VANETs is exposed in the open wireless communication environment, so it is vulnerable to several types of attacks. To balance the information security and privacy in the VANETs, there are a great number of conditional privacy-preserving authentication protocols proposed. However, only a few of them are resistant to quantum attacks, and these existing quantum-resistant works are unsatisfactory, ether are insecure or suffer from other problems. In this paper, we propose a secure and efficient lattice-based conditional privacy-preserving authentication protocol for the VANETs, which can achieve authentication and privacy protection, and a batch verification method is provided to further optimize the performance. Compared with the existing counterparts, our protocol is secure, efficient, and achieving lowest communication overhead. We provide several parameter sets, and the protocol achieves least execution time under some of them. We also show a security proof of the protocol in the random oracle model, based on the assume of inhomogeneous small integer solution problem.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100958"},"PeriodicalIF":5.8,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703540","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":"MetaCAN: An optimized adaptive hybrid metaheuristic-based intrusion detection system for CAN bus security","authors":"Kadir Ileri ,&nbsp;Abdur Rakib ,&nbsp;Soufiene Djahel","doi":"10.1016/j.vehcom.2025.100956","DOIUrl":"10.1016/j.vehcom.2025.100956","url":null,"abstract":"<div><div>The Controller Area Network (CAN) bus is a message-based protocol widely used in modern vehicles to facilitate communication between various Electronic Control Units (ECUs). However, its simplistic design lacks fundamental security measures, making it highly susceptible to cyberattacks. These vulnerabilities pose significant risks to vehicle safety, highlighting the critical need for implementation of effective intrusion detection systems (IDS). Therefore, in this paper, a machine learning based IDS optimized through an adaptive hybrid metaheuristic approach, named MetaCAN, is proposed to secure the CAN bus. MetaCAN leverages the complementary strengths of particle swarm optimization (PSO) for fast convergence and cuckoo search (CS) for robust global search to ensure effective hyperparameter tuning and model optimization. MetaCAN is evaluated using three real-world datasets including Survival Analysis, Car Hacking: Attack &amp; Defense Challenge 2020, and OTIDS. Unlike traditional binary detection systems, MetaCAN offers multi-class attack detection by identifying five distinct attack types including Denial of Service (DoS), fuzzy, masquerade, malfunction, and replay attacks. Moreover, the detection accuracy of the system is enhanced through a feature engineering process that introduces two effective features such as Time Interval and ID Repetition Count. The experimental results show that MetaCAN consistently outperforms existing IDS solutions targeted the same datasets, making it a promising solution for securing the CAN bus in real-world vehicular environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100956"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614566","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":"Energy efficiency optimization for UAV-mounted IRS assisted ISAC systems under statistical CSI","authors":"Peng Wang ,&nbsp;Huizhi Tang ,&nbsp;Demin Li ,&nbsp;Yihong Zhang ,&nbsp;Xuemin Chen","doi":"10.1016/j.vehcom.2025.100953","DOIUrl":"10.1016/j.vehcom.2025.100953","url":null,"abstract":"<div><div>Integrated Sensing and Communication (ISAC) systems are advantageous for enhancing both communication and sensing capabilities, but their performance is significantly impacted by signal blockages in dynamic vehicular environments. An Unmanned Aerial Vehicle (UAV)-mounted Intelligent Reflective Surface (IRS) for air-to-ground communication and sensing can significantly enhance coverage and deployment flexibility. However, the additional power consumption of the UAV-mounted IRS (UIRS) remains a challenge. To mitigate this, we propose a novel UIRS-assisted ISAC system that aims to maximize communication energy efficiency (EE) while meeting sensing quality-of-service (QoS) requirements by optimizing the UAV trajectory, IRS passive beamforming, and base station (BS) active beamforming. Due to the complex and dynamic nature of wireless channels, acquiring Channel State Information (CSI) is challenging, especially with the UAV's mobility and the passive mode of IRS. Therefore, statistical CSI is adopted in the proposed scheme. The optimization problem is reformulated into a tractable form and solved by decomposing it into three subproblems, which include using the Dinkelbach transformation for fractional programming in EE calculation, Successive Convex Approximation (SCA) for UAV trajectory optimization, and Semi-Definite Relaxation (SDR) for both active and passive beamforming designs. An alternating optimization (AO)-based framework iteratively solves all subproblems, with proven algorithm convergence and computational efficiency. Simulation results demonstrate that the proposed UIRS-assisted ISAC system significantly improves both communication and sensing performance compared to benchmark schemes.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100953"},"PeriodicalIF":5.8,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570530","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":"Energy and experimental trust-based task offloading in the domain of connected autonomous vehicles","authors":"Sachin Kumar Gupta ,&nbsp;Anuradha Banerjee","doi":"10.1016/j.vehcom.2025.100954","DOIUrl":"10.1016/j.vehcom.2025.100954","url":null,"abstract":"<div><div>Task offloading among connected and autonomous vehicles (CAVs) has recently gained much attention. The current literature in this context mostly optimizes only the criterion of energy and latency. Further, issues like connectivity and spontaneous attitude of selflessness have remained unexplored despite their importance and probable contribution to preserving vehicles' energy and reducing overall delay in completing the tasks. Therefore, the key objectives of the present study are maximization of residual energy and percentage of successful offloading, as well as minimization of energy consumption and delay. We have also considered trust, which has two components; efficiency and certainty. Efficiency is defined as the inverse of the estimated time duration required to complete the execution of the current task based on the history of the previous sessions. Certainty is related to the stability of the connection between the server and task off-loader vehicles and the selfless cooperation of the server, as revealed from the history of communication with the off-loader. Experimental results show that our proposed method of offloading tasks based on energy and experiential trust (OTEET) increases the offload success percentage and reduces cost by approximately 40%, which can be considered a significant improvement.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100954"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563852","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":"Optimizing end-to-end latency in C-V2X networks: A novel FD-RAN and MEC integration approach","authors":"Saber F. Mohammed ,&nbsp;Pan Zhiwen ,&nbsp;Haithm M. Al-Gunid ,&nbsp;Zeyad A.H. Qasem","doi":"10.1016/j.vehcom.2025.100955","DOIUrl":"10.1016/j.vehcom.2025.100955","url":null,"abstract":"<div><div>The increasing demand for low-latency services in cellular vehicle-to-everything (C-V2X) communications is crucial for the efficient operation of connected vehicles and autonomous driving systems. As C-V2X networks become integral to modern transportation infrastructure, minimizing end-to-end (E2E) latency remains a significant challenge in ensuring system reliability and effectiveness. To this end, we propose a novel approach that integrates a fully decoupled radio access network (FD-RAN) with multi-access edge computing (MEC) in C-V2X networks, aiming to optimize latency-sensitive applications. We apply a tractable analytical model for the E2E latency that accounts for latency contributions across the radio, backhaul, network, and processing layers. By leveraging FD-RAN's decoupled access and MEC's distributed processing, our approach effectively mitigates latency bottlenecks inherent in conventional RAN architectures. Additionally, we propose a stochastic optimization-based resource allocation method using Lyapunov techniques and Markov decision processes to dynamically manage base station selection and bandwidth allocation, thereby enhancing the system performance. Simulation results demonstrate that FD-RAN with MEC significantly reduces E2E latency compared with conventional RAN architectures, even under high traffic densities, while maintaining high data rates. These findings validate the proposed approach and offer key insights for developing low-latency infrastructures for next-generation V2X applications.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100955"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570531","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":"Dynamic offloading strategy in SAGIN-based emergency VEC: A multi-UAV clustering and collaborative computing approach","authors":"Zhenzheng Shi,&nbsp;Liang Wang,&nbsp;Yaguang Lin,&nbsp;Anna Cai,&nbsp;Jiamin Fan,&nbsp;Cong Liu","doi":"10.1016/j.vehcom.2025.100952","DOIUrl":"10.1016/j.vehcom.2025.100952","url":null,"abstract":"<div><div>Mobile edge computing (MEC) technology can provide stable and efficient computing services for ground vehicles and users. However, maintaining stable MEC services becomes challenging in scenarios where ground MEC servers are damaged or unavailable, such as in post-disaster or remote areas. To tackle this issue, this paper proposes a novel space-air-ground integrated network (SAGIN) based emergency vehicular edge computing (VEC) framework, leveraging the rapid deployment characteristic of unmanned aerial vehicle (UAV) to provide VEC services for ground vehicles. A distance-based UAV clustering (DUC) algorithm is designed for efficient multi-UAV collaboration, executed by low earth orbit (LEO) satellite with wide coverage. Within each cluster, a task splitting algorithm based on a novel expected computing delay (ECD) metric is performed by the cluster-head UAV (CHU). Focusing on the issue of limited line-of-sight (LoS) range of UAV and computing sustainability during vehicle moving, we propose a dynamic offloading strategy. Simulation results show that the proposed framework enhances UAV utilization by 60% and significantly reduces task process delays across varying scenarios.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100952"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563851","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":"Multi-objective resource allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks","authors":"Tong Wang","doi":"10.1016/j.vehcom.2025.100951","DOIUrl":"10.1016/j.vehcom.2025.100951","url":null,"abstract":"<div><div>In multi-UAV-assisted air-ground integrated in-band full-duplex (IBFD) OFDMA networks, both uplink and downlink performances are critical and must be simultaneously considered. This study addresses effective resource allocation in such networks to maximize the total system uplink and downlink rates by jointly optimizing subcarrier assignment and power control. Given the significant trade-off between uplink and downlink transmissions owing to self-interference in IBFD systems and intercell interference, we formulate the resource allocation problem as a multi-objective optimization problem (MOOP), aiming to jointly maximize the uplink and downlink performances. To achieve Pareto optimal solutions, we employ the weighted Tchebycheff technique to transform the MOOP into a single-objective optimization problem (SOOP) and solve it using Successive Convex Approximation (SCA) within a Block Coordinate Descent (BCD) framework. This approach iteratively optimizes the subcarrier assignment and power control and effectively manages the trade-offs between uplink and downlink rates. The proposed method demonstrates the ability to achieve an efficient balance in resource allocation. Simulation results show that our method can obtain Pareto optimal solutions, demonstrating favorable performance trade-offs and fairness under various interference conditions, thereby improving the overall system performance in multi-UAV-assisted air-ground integrated OFDMA networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100951"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534422","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":"Deep reinforcement learning based migration and execution decisions for multi-hop task offloading in mobile vehicle edge computing","authors":"Wenjie Zhou,&nbsp;Tian Zhang,&nbsp;Zekun Lu,&nbsp;Linbo Zhai","doi":"10.1016/j.vehcom.2025.100950","DOIUrl":"10.1016/j.vehcom.2025.100950","url":null,"abstract":"<div><div>As the Internet of Things (IoT) drives the development of Vehicular Edge Computing (VEC), there is a surge in computational demand from emerging in-vehicle applications. Most existing studies do not fully consider the frequent changes in network topology under high mobility of vehicles and the underutilization of idle resources by single-hop offloading. To this end, we propose a task offloading scheme for vehicular edge computing based on multi-hop offloading. The scheme allows task vehicles to offload tasks to service vehicles with excess idle resources outside the communication range, and adapts to dynamic changes in network topology by introducing the concept of neighboring vehicle connection time. This study aims to minimize the delayed energy consumption utility value of the task under the conditions of satisfying the maximum task delay limit, vehicle computational and storage resource constraints. In response to this NP-hard problem, a two-stage reinforcement learning strategy MOCDD (combining Deep Q Network (DQN) and Deep Deterministic Policy Gradient (DDPG)) is proposed to divide the mixed action space into pure discrete and pure continuous action space to determine task migration, executive decision and vehicle transmission power. Simulation results verify the effectiveness of the proposed scheme.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"55 ","pages":"Article 100950"},"PeriodicalIF":5.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563853","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}