The rapid development of Intelligent Transportation Systems (ITS) and autonomous driving technologies has made secure authentication and privacy protection of vehicle identities in Vehicular Ad Hoc Networks (VANETs) a hot research issue. Existing solutions typically rely on pseudonym-based approaches. These approaches incur large storage overhead and computational costs, which limit their scalability and efficiency. To address this problem, this paper proposes a novel anonymous authentication scheme in VANETs that synergistically integrates zero-knowledge proof (ZKP) and blockchain technology. An identity-based polynomial commitment is used in the present scheme to achieve privacy-preserving authentication, which ensures the anonymity of the vehicle without revealing sensitive information. Additionally, an identity-based signature algorithm, based on the Gap Diffie-Hellman (GDH) problem, ensures session unlinkability, enhancing connection security. Incorporating the Merkle Patricia Trie (MPT) into the blockchain framework optimizes data retrieval efficiency while minimizing storage and computational burdens on the central server. Blockchain's inherent immutability and transparency further enhance data integrity and security.
{"title":"A privacy-enhanced authentication scheme for VANETs based on blockchain and zero-knowledge proof","authors":"Shangping Wang , Qi Huang, Ruoxin Yan, Juanjuan Ma, Xiaoling Xie","doi":"10.1016/j.vehcom.2025.100976","DOIUrl":"10.1016/j.vehcom.2025.100976","url":null,"abstract":"<div><div>The rapid development of Intelligent Transportation Systems (ITS) and autonomous driving technologies has made secure authentication and privacy protection of vehicle identities in Vehicular Ad Hoc Networks (VANETs) a hot research issue. Existing solutions typically rely on pseudonym-based approaches. These approaches incur large storage overhead and computational costs, which limit their scalability and efficiency. To address this problem, this paper proposes a novel anonymous authentication scheme in VANETs that synergistically integrates zero-knowledge proof (ZKP) and blockchain technology. An identity-based polynomial commitment is used in the present scheme to achieve privacy-preserving authentication, which ensures the anonymity of the vehicle without revealing sensitive information. Additionally, an identity-based signature algorithm, based on the Gap Diffie-Hellman (GDH) problem, ensures session unlinkability, enhancing connection security. Incorporating the Merkle Patricia Trie (MPT) into the blockchain framework optimizes data retrieval efficiency while minimizing storage and computational burdens on the central server. Blockchain's inherent immutability and transparency further enhance data integrity and security.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100976"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268464","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 : 2025-12-01Epub Date: 2025-10-10DOI: 10.1016/j.vehcom.2025.100980
Aiswarya S. Nair , Sabu M. Thampi , Jithu Vijay V. P.
GPS spoofing remains a significant and persistent threat to Internet of Drones (IoD), which compromises navigation integrity, security, and reliability. Drones, constrained by limited computational resources and power, demand innovative solutions to combat this easily exploitable vulnerability. Existing detection methods lack computational efficiency, contextual intelligence, and collaborative validation, leading to high false positives and low adaptability. In this paper, we propose a context-aware GPS spoofing detection and mitigation framework, SoCoMNNet, that integrates Memristive Neural Networks (MNNs) and a SocioCognitive fuzzy inference system for trust-driven behaviour analysis. The MNN module, deployed on each drone, detects navigation inconsistencies with minimal computational overhead, while the SocioCognitive system at the Ground Control Station (GCS) evaluates drone's behaviour in terms of Ability, Benevolence, and Integrity (ABI) to differentiate adversarial GPS spoofing from mission deviations. The predictions from the MNN and the behaviour assessment are combined using a weighted average, where both are given equal importance. In this way, the final result considers what the model predicts as well as how the drone is actually behaving, making GPS spoofing detection more accurate and context-aware. The contextual understanding provided by the SocioCognitive fuzzy system helps differentiate intentional deviations from unexpected ones, enhancing the overall resilience of the system. We have also developed a Kyber Post-Quantum Cryptography (PQC) secured GPS spoofing mitigation mechanism that helps drones to recover authentic GPS data during spoofing attacks. We evaluated the performance of MNN using MemTorch for memristor-based neural modelling, and NeuroSIM for hardware-level simulation and resource analysis. The fuzzy inference engine runs 27 rules and deduces five drone behaviours such as Discard, Unsatisfactory, Satisfactory, Reliable, and Ideal. Incorporating this context awareness into the detection process enables SoCoMNNet to reduce false positives during GPS spoofing detection. A statistical t-test was performed to show the impact of the proposed detection approach. The Kyber PQC mitigation approach was evaluated on Raspberry Pi 4 in terms of computation cost, communication overhead, and storage requirements. The results show reduced execution time, higher computational efficiency, lower memory usage, and stronger system security. Our integrated solution delivers a resilient and computationally efficient security framework for IoD in adversarial GPS spoofing environments.
GPS欺骗仍然是无人机互联网（IoD）的一个重大而持久的威胁，它损害了导航的完整性、安全性和可靠性。无人机受到有限的计算资源和能力的限制，需要创新的解决方案来对抗这种容易被利用的漏洞。现有检测方法缺乏计算效率、上下文智能和协同验证，导致误报率高、适应性低。在本文中，我们提出了一个上下文感知的GPS欺骗检测和缓解框架SoCoMNNet，它集成了记忆神经网络（MNNs）和社会认知模糊推理系统，用于信任驱动的行为分析。部署在每架无人机上的MNN模块，以最小的计算开销检测导航不一致，而地面控制站（GCS）的社会认知系统（social cognitive system）根据能力、仁慈和完整性（ABI）评估无人机的行为，以区分对抗性GPS欺骗和任务偏差。来自MNN的预测和行为评估使用加权平均值进行组合，其中两者具有同等的重要性。通过这种方式，最终结果考虑了模型预测的内容以及无人机的实际行为，使GPS欺骗检测更加准确和具有上下文感知能力。社会认知模糊系统提供的上下文理解有助于区分有意偏差和意外偏差，增强系统的整体弹性。我们还开发了Kyber后量子加密（PQC）安全的GPS欺骗缓解机制，帮助无人机在欺骗攻击期间恢复真实的GPS数据。我们使用MemTorch进行基于忆阻器的神经建模，使用NeuroSIM进行硬件级仿真和资源分析，评估了MNN的性能。模糊推理引擎运行27条规则，演绎出丢弃、不满意、满意、可靠、理想五种无人机行为。将这种上下文感知整合到检测过程中，SoCoMNNet可以减少GPS欺骗检测过程中的误报。进行统计t检验以显示所提出的检测方法的影响。Kyber PQC缓解方法在Raspberry Pi 4上进行了计算成本、通信开销和存储需求方面的评估。结果表明，减少了执行时间，提高了计算效率，降低了内存使用，增强了系统安全性。我们的集成解决方案为对抗GPS欺骗环境中的IoD提供了弹性和计算效率高的安全框架。
{"title":"SoCoMNNet: A SocioCognitive and memristive neural network-based context-aware GPS spoofing detection and mitigation in the Internet of drones","authors":"Aiswarya S. Nair , Sabu M. Thampi , Jithu Vijay V. P.","doi":"10.1016/j.vehcom.2025.100980","DOIUrl":"10.1016/j.vehcom.2025.100980","url":null,"abstract":"<div><div>GPS spoofing remains a significant and persistent threat to Internet of Drones (IoD), which compromises navigation integrity, security, and reliability. Drones, constrained by limited computational resources and power, demand innovative solutions to combat this easily exploitable vulnerability. Existing detection methods lack computational efficiency, contextual intelligence, and collaborative validation, leading to high false positives and low adaptability. In this paper, we propose a context-aware GPS spoofing detection and mitigation framework, SoCoMNNet, that integrates Memristive Neural Networks (MNNs) and a SocioCognitive fuzzy inference system for trust-driven behaviour analysis. The MNN module, deployed on each drone, detects navigation inconsistencies with minimal computational overhead, while the SocioCognitive system at the Ground Control Station (GCS) evaluates drone's behaviour in terms of Ability, Benevolence, and Integrity (ABI) to differentiate adversarial GPS spoofing from mission deviations. The predictions from the MNN and the behaviour assessment are combined using a weighted average, where both are given equal importance. In this way, the final result considers what the model predicts as well as how the drone is actually behaving, making GPS spoofing detection more accurate and context-aware. The contextual understanding provided by the SocioCognitive fuzzy system helps differentiate intentional deviations from unexpected ones, enhancing the overall resilience of the system. We have also developed a Kyber Post-Quantum Cryptography (PQC) secured GPS spoofing mitigation mechanism that helps drones to recover authentic GPS data during spoofing attacks. We evaluated the performance of MNN using MemTorch for memristor-based neural modelling, and NeuroSIM for hardware-level simulation and resource analysis. The fuzzy inference engine runs 27 rules and deduces five drone behaviours such as Discard, Unsatisfactory, Satisfactory, Reliable, and Ideal. Incorporating this context awareness into the detection process enables SoCoMNNet to reduce false positives during GPS spoofing detection. A statistical t-test was performed to show the impact of the proposed detection approach. The Kyber PQC mitigation approach was evaluated on Raspberry Pi 4 in terms of computation cost, communication overhead, and storage requirements. The results show reduced execution time, higher computational efficiency, lower memory usage, and stronger system security. Our integrated solution delivers a resilient and computationally efficient security framework for IoD in adversarial GPS spoofing environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100980"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321171","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 : 2025-12-01Epub Date: 2025-09-17DOI: 10.1016/j.vehcom.2025.100969
Tayyaba Khurshid , Waqas Ahmed , Rizwan Ahmad , Muhammad Mahtab Alam , Joel J.P.C. Rodrigues
In a Multiple Unmanned Air Vehicle (m-UAV) system, employing a centralized communication approach poses many challenges such as communication range limitations, energy efficiency, latency, etc. due to limited UAV resources. On the other hand, a distributed consensus approach has the ability to overcome these limitations and possesses numerous advantages if appropriate coordination mechanism among the UAVs is employed. Therefore, in this paper, we investigate joint optimization of 3D trajectory and UAV resources using a distributed consensus approach. We assume that User Devices (UDs) compute a portion of the task locally and offload the remaining part to the nearby Mobile Edge Computing (MEC) based UAV. Considering UAV dynamics and environmental constraints, a Deep Deterministic Policy Gradient (DDPG) is presented based on Distributed Dynamic Consensus (DDC) approach that utilizes consensus theory for distributed computing. We classified DDC into three cases namely; Distributed Velocity Consensus (DVC), Distributed Error Consensus (DEC), and Distributed Dynamic Velocity Consensus (DDVC). The performance of all three cases based on cost percentage (cost is the sum of normalized time delay and normalized energy consumption) and observed that DEC achieves minimum cost i.e., 40.62 whereas DVC and DDVC settled at 48.18 and 44.06 respectively. We further investigate the performance of DEC in partially connected, moderately connected, and fully connected networks. With centralized and autonomous decision-making scenario as a benchmark, results show that the DEC in the partially connected scenario converges faster with a lower cost.
{"title":"Distributed Dynamic Consensus (DDC) protocol for multi-UAV 3D trajectory planning and resource allocation","authors":"Tayyaba Khurshid , Waqas Ahmed , Rizwan Ahmad , Muhammad Mahtab Alam , Joel J.P.C. Rodrigues","doi":"10.1016/j.vehcom.2025.100969","DOIUrl":"10.1016/j.vehcom.2025.100969","url":null,"abstract":"<div><div>In a Multiple Unmanned Air Vehicle (m-UAV) system, employing a centralized communication approach poses many challenges such as communication range limitations, energy efficiency, latency, etc. due to limited UAV resources. On the other hand, a distributed consensus approach has the ability to overcome these limitations and possesses numerous advantages if appropriate coordination mechanism among the UAVs is employed. Therefore, in this paper, we investigate joint optimization of 3D trajectory and UAV resources using a distributed consensus approach. We assume that User Devices (UDs) compute a portion of the task locally and offload the remaining part to the nearby Mobile Edge Computing (MEC) based UAV. Considering UAV dynamics and environmental constraints, a Deep Deterministic Policy Gradient (DDPG) is presented based on Distributed Dynamic Consensus (DDC) approach that utilizes consensus theory for distributed computing. We classified DDC into three cases namely; Distributed Velocity Consensus (DVC), Distributed Error Consensus (DEC), and Distributed Dynamic Velocity Consensus (DDVC). The performance of all three cases based on cost percentage (cost is the sum of normalized time delay and normalized energy consumption) and observed that DEC achieves minimum cost i.e., 40.62 whereas DVC and DDVC settled at 48.18 and 44.06 respectively. We further investigate the performance of DEC in partially connected, moderately connected, and fully connected networks. With centralized and autonomous decision-making scenario as a benchmark, results show that the DEC in the partially connected scenario converges faster with a lower cost.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100969"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221464","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 : 2025-12-01Epub Date: 2025-09-15DOI: 10.1016/j.vehcom.2025.100968
Mohsen Eskandari , Andrey V. Savkin , Mohammad Deghat
Low latency, reliable, and stable communication are essential for autonomous driving and mission accomplishment of Internet-of-Vehicles (IoVs) in smart cities. Therefore, future wireless networks will work based on quasi-optic millimeter wave (mmWave) signals for high-rate data transfer. However, given the mobility of vehicles, the mmWave links are prone to outages as they intrinsically rely on directional beamforming to line-of-sight (LoS) paths. Notably, fragile wireless links in dense urban canyons expose autonomous vehicles to safety risks. An unmanned aerial vehicle (UAV) equipped with a reconfigurable holographic surface (RHS) is navigated for establishing aerial LoS links for IoVs. RHS performs beamforming by adjusting the radiation patterns through the holographic surface, so it is energy efficient. The UAV-RHS is supported by terrestrial reconfigurable intelligent surfaces (RISs) installed on building facades, which are utilized to improve coverage and link reliability. The UAV’s navigation objectives are maintaining valid LoS links for IoVs, ensuring quality of service, and minimizing energy consumption. However, an obstacle-free kinematics-aware smooth trajectory, subject to motion constraints, is required for UAV navigation in dense urban environments. Satisfying these navigation objectives and constraints makes the trajectory design with valid LoS links a non-convex NP-hard optimization problem. To address this, we propose, for the first time, training generative adversarial networks (GANs) to generate valid paths in real time. State feedback control with quadratic optimization is proposed to smooth the trajectory. Simulation results are provided to evaluate the proposed method.
{"title":"Joint smooth trajectory design and wireless communication control for mobile internet of vehicles assisted by a UAV and ground RISs","authors":"Mohsen Eskandari , Andrey V. Savkin , Mohammad Deghat","doi":"10.1016/j.vehcom.2025.100968","DOIUrl":"10.1016/j.vehcom.2025.100968","url":null,"abstract":"<div><div>Low latency, reliable, and stable communication are essential for autonomous driving and mission accomplishment of Internet-of-Vehicles (IoVs) in smart cities. Therefore, future wireless networks will work based on quasi-optic millimeter wave (mmWave) signals for high-rate data transfer. However, given the mobility of vehicles, the mmWave links are prone to outages as they intrinsically rely on directional beamforming to line-of-sight (LoS) paths. Notably, fragile wireless links in dense urban canyons expose autonomous vehicles to safety risks. An unmanned aerial vehicle (UAV) equipped with a reconfigurable holographic surface (RHS) is navigated for establishing aerial LoS links for IoVs. RHS performs beamforming by adjusting the radiation patterns through the holographic surface, so it is energy efficient. The UAV-RHS is supported by terrestrial reconfigurable intelligent surfaces (RISs) installed on building facades, which are utilized to improve coverage and link reliability. The UAV’s navigation objectives are maintaining valid LoS links for IoVs, ensuring quality of service, and minimizing energy consumption. However, an obstacle-free kinematics-aware smooth trajectory, subject to motion constraints, is required for UAV navigation in dense urban environments. Satisfying these navigation objectives and constraints makes the trajectory design with valid LoS links a non-convex NP-hard optimization problem. To address this, we propose, for the first time, training generative adversarial networks (GANs) to generate valid paths in real time. State feedback control with quadratic optimization is proposed to smooth the trajectory. Simulation results are provided to evaluate the proposed method.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100968"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099644","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 : 2025-12-01Epub Date: 2025-10-10DOI: 10.1016/j.vehcom.2025.100979
Youqiang Hu
Vehicular federated learning is able to deal with the data shortage dilemma in practical Artificial Intelligence (AI) projects in Internet of Vehicles (IoV) scenarios, but there is a bottleneck in this training paradigm, that is, the energy consumption issue. Since the training process is carried out on vehicles and the training tasks are generally computation-intensive, the battery lives of vehicles will be greatly affected if they participate in training. To improve the sustainability of the participants, split learning is introduced to the procedure of vehicular federated learning in this paper. Split learning divides the trained AI model into two parts. One is retained on terminals, and the other is sent to the cloud servers for remote training. Since the computation workload is reduced, the energy consumptions of participants are lowered. This training paradigm is called Vehicular Federated Split Learning (VFSL). Then, we model the computation and communication processes of VFSL and derive the energy consumption minimization problem. The estimated Channel State Information (CSI) between high-moving vehicles and Road Side Units (RSUs) is generally inaccurate. The imperfect CSI makes the formulated problem a stochastic mixed integer nonlinear programming problem, which is hard to solve. Therefore, we propose a resource allocation and model split strategy based on the Constrained Stochastic Successive Convex Approximation (CSSCA) and greedy algorithms. Simulation results demonstrate that the proposed strategy is able to achieve higher energy efficiency compared to the existing strategies in the case of imperfect CSI.
{"title":"Resource allocation and model split for energy-efficient federated split learning in Internet of Vehicles with imperfect CSI","authors":"Youqiang Hu","doi":"10.1016/j.vehcom.2025.100979","DOIUrl":"10.1016/j.vehcom.2025.100979","url":null,"abstract":"<div><div>Vehicular federated learning is able to deal with the data shortage dilemma in practical Artificial Intelligence (AI) projects in Internet of Vehicles (IoV) scenarios, but there is a bottleneck in this training paradigm, that is, the energy consumption issue. Since the training process is carried out on vehicles and the training tasks are generally computation-intensive, the battery lives of vehicles will be greatly affected if they participate in training. To improve the sustainability of the participants, split learning is introduced to the procedure of vehicular federated learning in this paper. Split learning divides the trained AI model into two parts. One is retained on terminals, and the other is sent to the cloud servers for remote training. Since the computation workload is reduced, the energy consumptions of participants are lowered. This training paradigm is called Vehicular Federated Split Learning (VFSL). Then, we model the computation and communication processes of VFSL and derive the energy consumption minimization problem. The estimated Channel State Information (CSI) between high-moving vehicles and Road Side Units (RSUs) is generally inaccurate. The imperfect CSI makes the formulated problem a stochastic mixed integer nonlinear programming problem, which is hard to solve. Therefore, we propose a resource allocation and model split strategy based on the Constrained Stochastic Successive Convex Approximation (CSSCA) and greedy algorithms. Simulation results demonstrate that the proposed strategy is able to achieve higher energy efficiency compared to the existing strategies in the case of imperfect CSI.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100979"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321169","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 : 2025-12-01Epub Date: 2025-10-15DOI: 10.1016/j.vehcom.2025.100981
Peiying Zhang , Renzhuang Yuan , Lizhuang Tan , Konstantin Igorevich Kostromitin , Athanasios V. Vasilakos , Jian Wang
With the rapid development of Intelligent Vehicles and Mobile Edge Computing, the Internet of Vehicles (IoV) faces numerous challenges when handling computationally intensive tasks, primarily including limited computing resources and dynamic requirement distributions in the IoV system. To address these challenges, this paper proposes a novel edge–cloud collaborative offloading framework for the Internet of Vehicles, named TP-IoV. This framework achieves proactive optimization of computation task offloading by combining time series task prediction with an adaptive decision mechanism. Specifically, TP-IoV utilizes the Informer model to predict future task characteristics and employs the Twin Delayed Deep Deterministic Policy Gradient algorithm to determine the optimal allocation strategy for tasks among local vehicles, edge nodes, and cloud servers. Simulation results demonstrate that under high load and strict latency conditions, TP-IoV significantly outperforms existing baseline methods in reducing task latency and improving the task completion success rate. This work provides a scalable and proactive solution for latency-sensitive applications in dynamic Internet of Vehicles environments.
随着智能汽车和移动边缘计算的快速发展，车联网在处理计算密集型任务时面临着许多挑战，主要包括车联网系统中有限的计算资源和动态的需求分布。为了应对这些挑战，本文提出了一种新型的车联网边缘云协作卸载框架，称为TP-IoV。该框架将时间序列任务预测与自适应决策机制相结合，实现了计算任务卸载的主动优化。具体而言，TP-IoV利用Informer模型预测未来任务特征，并采用Twin Delayed Deep Deterministic Policy Gradient算法确定任务在本地车辆、边缘节点和云服务器之间的最优分配策略。仿真结果表明，在高负载和严格延迟条件下，TP-IoV在降低任务延迟和提高任务完成成功率方面明显优于现有的基线方法。这项工作为动态车联网环境中对延迟敏感的应用提供了可扩展的主动解决方案。
{"title":"TP-IoV: A task prediction-oriented cloud-edge collaborative offloading framework for Internet of vehicles","authors":"Peiying Zhang , Renzhuang Yuan , Lizhuang Tan , Konstantin Igorevich Kostromitin , Athanasios V. Vasilakos , Jian Wang","doi":"10.1016/j.vehcom.2025.100981","DOIUrl":"10.1016/j.vehcom.2025.100981","url":null,"abstract":"<div><div>With the rapid development of Intelligent Vehicles and Mobile Edge Computing, the Internet of Vehicles (IoV) faces numerous challenges when handling computationally intensive tasks, primarily including limited computing resources and dynamic requirement distributions in the IoV system. To address these challenges, this paper proposes a novel edge–cloud collaborative offloading framework for the Internet of Vehicles, named TP-IoV. This framework achieves proactive optimization of computation task offloading by combining time series task prediction with an adaptive decision mechanism. Specifically, TP-IoV utilizes the Informer model to predict future task characteristics and employs the Twin Delayed Deep Deterministic Policy Gradient algorithm to determine the optimal allocation strategy for tasks among local vehicles, edge nodes, and cloud servers. Simulation results demonstrate that under high load and strict latency conditions, TP-IoV significantly outperforms existing baseline methods in reducing task latency and improving the task completion success rate. This work provides a scalable and proactive solution for latency-sensitive applications in dynamic Internet of Vehicles environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100981"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363500","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 : 2025-12-01Epub Date: 2025-09-01DOI: 10.1016/j.vehcom.2025.100966
Abishek Subramanian, Aurenice Oliveira
Vehicle to Infrastructure (V2I) connectivity has historically relied on Dedicated Short Range Communication (DSRC) and more recently Cellular Vehicle to Everything (C-V2X). However, DSRC adoption has slowed due to high deployment costs, whereas C-V2X, limited to the 5.9 GHz sub 6 GHz band, provides modest data rates mainly suitable for safety critical messages. Emerging V2I services, such as high resolution sensor sharing and cooperative perception, demand multi gigabit throughput to transfer large volumes of data (4–10 GB) between vehicles and Mobile Edge Computing (MEC) servers, requirements exceeding the capacity of sub-6 GHz technologies. This study explores a novel distributed architecture utilizing a federated learning paradigm for optimizing mmWave beamforming processes in V2I communication systems. By leveraging multiple non-RF modality sensors (GPS and LiDAR) and deep learning models, this approach aims to enhance the global model's adaptability and reduce the sub-6 GHz channel usage. The proposed system uses client-biased selection strategies, including MaxLoss and Heuristic Multi-Arm Bandit, to train and update the global model, demonstrating significant improvements in convergence rates and overall performance. Simulation results using the Infocom FLASH dataset validate the framework's efficiency, highlighting its potential for real-world deployment in dynamic environments.
{"title":"A novel distributed architecture incorporating deep learning and biased selection for vehicular communication mmWaves beamforming","authors":"Abishek Subramanian, Aurenice Oliveira","doi":"10.1016/j.vehcom.2025.100966","DOIUrl":"10.1016/j.vehcom.2025.100966","url":null,"abstract":"<div><div>Vehicle to Infrastructure (V2I) connectivity has historically relied on Dedicated Short Range Communication (DSRC) and more recently Cellular Vehicle to Everything (C-V2X). However, DSRC adoption has slowed due to high deployment costs, whereas C-V2X, limited to the 5.9 GHz sub 6 GHz band, provides modest data rates mainly suitable for safety critical messages. Emerging V2I services, such as high resolution sensor sharing and cooperative perception, demand multi gigabit throughput to transfer large volumes of data (4–10 GB) between vehicles and Mobile Edge Computing (MEC) servers, requirements exceeding the capacity of sub-6 GHz technologies. This study explores a novel distributed architecture utilizing a federated learning paradigm for optimizing mmWave beamforming processes in V2I communication systems. By leveraging multiple non-RF modality sensors (GPS and LiDAR) and deep learning models, this approach aims to enhance the global model's adaptability and reduce the sub-6 GHz channel usage. The proposed system uses client-biased selection strategies, including MaxLoss and Heuristic Multi-Arm Bandit, to train and update the global model, demonstrating significant improvements in convergence rates and overall performance. Simulation results using the Infocom FLASH dataset validate the framework's efficiency, highlighting its potential for real-world deployment in dynamic environments.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100966"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145009039","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 : 2025-12-01Epub Date: 2025-09-17DOI: 10.1016/j.vehcom.2025.100970
Zawiyah Saharuna , Tohari Ahmad , Royyana Muslim Ijtihadie
Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS) but remain highly vulnerable to cyberattacks, such as malicious attacks and position falsification. Detection is hindered by high-dimensional traffic data and severe class imbalance. Existing intrusion detection methods often overlook feature importance, limiting adaptability to different attack types. This study proposes an adaptive Intrusion Detection System (IDS) integrating SHAP-based feature selection with a MASV-weighted SMOTE technique. To the best of our knowledge, this is the first framework to leverage SHAP values not only for feature selection but also to guide class rebalancing during synthetic sample generation. Unlike conventional approaches, which treat all features equally, our method prioritizes features based on their Mean Absolute SHAP Values (MASV) in both selection and oversampling. Evaluated on CICIDS-2017 and validated on VeReMi, the framework demonstrates strong generalizability between datasets. It reduces feature dimensionality by up to 80% (78 to 15 features) while maintaining 99.91% accuracy, achieving up to 50.79% faster training and real-time inference below 0.1 ms per instance. MASV-weighted SMOTE transforms minority class detection performance, elevating the Infiltration attack F1-score from 0 to 88.89% and PR-AUC from 4.43% to 100%. These results outperform baseline models, enabling accurate, efficient, and interpretable IDS for VANETs security applications.
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Pub Date : 2025-12-01Epub Date: 2025-09-17DOI: 10.1016/j.vehcom.2025.100971
Md. Thouhidur Rahman , A.F.M. Shahen Shah , Muhammet Ali Karabulut , Haci Ilhan
In 5G and beyond, unmanned aerial vehicles (UAVs) are highly valued for their communication capabilities, affordability, and deployment flexibility. Multi-UAV systems, which operate in ad-hoc networks known as UAV ad-hoc networks or flying ad-hoc networks (FANETs), represent some of the most promising technologies both currently and in the future. In disaster situations, it will be crucial to set up temporary UAV-based emergency flying base stations (BSs), provide wireless coverage in cellular networks, and establish communication relays for long-distance data transmission. To establish this network, efficient communication between the UAVs is the most vital point. Additionally, due to mobility, the topology changes frequently, leading to potential collisions and packet losses. Therefore, in this paper, a minimum distance clustering scheme (MDCS)-based FANET is proposed, where the topology is controlled by a back-off mechanism and network connectivity is maintained even when the UAVs are moving at different altitudes by calculating the relative velocity on a 3D platform while considering the randomized path-based 3D mobility model. An efficient cluster build-up process and a method for determining the position of the cluster head (CH) are introduced to control the cluster proficiently. An analytical study is performed considering Rayleigh, Nakagami-m, and Rician fading channels. Moreover, the obtained Monte Carlo simulation results justify the analytical findings. Finally, the simulations show better results than existing work in terms of throughput with changes in distance, velocity, and the number of UAVs, as well as outage probability, packet dropping rate, and delay. In the case of the Rician fading channel, for 40 UAVs, a cluster size of 7 or fewer is more favorable, showing a maximum connection distance of 165 m, a maximum throughput of 10.2 Mbps, and a maximum delay of 55.57 ms.
{"title":"FANET-enabled cluster-based emergency communication with 3D mobility in 5G and beyond","authors":"Md. Thouhidur Rahman , A.F.M. Shahen Shah , Muhammet Ali Karabulut , Haci Ilhan","doi":"10.1016/j.vehcom.2025.100971","DOIUrl":"10.1016/j.vehcom.2025.100971","url":null,"abstract":"<div><div>In 5G and beyond, unmanned aerial vehicles (UAVs) are highly valued for their communication capabilities, affordability, and deployment flexibility. Multi-UAV systems, which operate in ad-hoc networks known as UAV ad-hoc networks or flying ad-hoc networks (FANETs), represent some of the most promising technologies both currently and in the future. In disaster situations, it will be crucial to set up temporary UAV-based emergency flying base stations (BSs), provide wireless coverage in cellular networks, and establish communication relays for long-distance data transmission. To establish this network, efficient communication between the UAVs is the most vital point. Additionally, due to mobility, the topology changes frequently, leading to potential collisions and packet losses. Therefore, in this paper, a minimum distance clustering scheme (MDCS)-based FANET is proposed, where the topology is controlled by a back-off mechanism and network connectivity is maintained even when the UAVs are moving at different altitudes by calculating the relative velocity on a 3D platform while considering the randomized path-based 3D mobility model. An efficient cluster build-up process and a method for determining the position of the cluster head (CH) are introduced to control the cluster proficiently. An analytical study is performed considering Rayleigh, Nakagami-m, and Rician fading channels. Moreover, the obtained Monte Carlo simulation results justify the analytical findings. Finally, the simulations show better results than existing work in terms of throughput with changes in distance, velocity, and the number of UAVs, as well as outage probability, packet dropping rate, and delay. In the case of the Rician fading channel, for 40 UAVs, a cluster size of 7 or fewer is more favorable, showing a maximum connection distance of 165 m, a maximum throughput of 10.2 Mbps, and a maximum delay of 55.57 ms.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100971"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099646","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 : 2025-12-01Epub Date: 2025-10-01DOI: 10.1016/j.vehcom.2025.100977
Mobasshir Mahbub , Mir Md. Saym , Sarwar Jahan , Anup Kumar Paul , Alireza Vahid , Seyyedali Hosseinalipour , Bobby Barua , Hen-Geul Yeh , Raed M. Shubair , Tarik Taleb , Aryan Kaushik , Mohammed H. Alsharif , M. Shariful Islam , Russel Reza Mahmud , Dusit Niyato
Unmanned Aerial Vehicles (UAVs) have emerged as transformative tools in wireless communication systems, revolutionizing the landscape of next-generation networks, including 6G and beyond. This survey comprehensively examines the technical advancements, challenges, and future directions of UAV-assisted wireless communications. It begins with analyzing UAV characteristics, such as flight dynamics, payload capacity, and power systems, and explores their pivotal role in enabling efficient connectivity across terrestrial, aerial, and maritime domains. The survey then delves into enabling technologies like advanced antenna designs, beamforming techniques, channel modeling, energy consumption models, and mobility optimization, emphasizing their necessity for achieving seamless UAV-to-ground, UAV-to-UAV, and UAV-to-satellite interactions. It further discusses regulatory frameworks and standardization efforts by global entities to address safety, spectrum allocation, and privacy concerns. Innovative routing protocols, including AI-driven and software-defined networking approaches, are analyzed, highlighting their potential to enhance scalability, reduce latency, and optimize resource management in dynamic UAV networks. This work identifies significant challenges such as energy efficiency, secure communication in hostile environments, and trajectory optimization while navigating complex three-dimensional (3D) spaces. The survey finally proposes directions for future research, including the exploration of sub-THz and THz communication, cross-layer routing, and the integration of UAVs with emerging networking paradigms. By synthesizing lessons learned and outlining unresolved questions, this paper serves as a resource for advancing UAV-enabled connectivity and unlocking new capabilities for ubiquitous and resilient wireless networks.
{"title":"UAV-assisted wireless communications in the 6G-and-beyond era: An extensive survey on characteristics, standardization and regulations, enabling technologies, challenges, and future directions","authors":"Mobasshir Mahbub , Mir Md. Saym , Sarwar Jahan , Anup Kumar Paul , Alireza Vahid , Seyyedali Hosseinalipour , Bobby Barua , Hen-Geul Yeh , Raed M. Shubair , Tarik Taleb , Aryan Kaushik , Mohammed H. Alsharif , M. Shariful Islam , Russel Reza Mahmud , Dusit Niyato","doi":"10.1016/j.vehcom.2025.100977","DOIUrl":"10.1016/j.vehcom.2025.100977","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have emerged as transformative tools in wireless communication systems, revolutionizing the landscape of next-generation networks, including 6G and beyond. This survey comprehensively examines the technical advancements, challenges, and future directions of UAV-assisted wireless communications. It begins with analyzing UAV characteristics, such as flight dynamics, payload capacity, and power systems, and explores their pivotal role in enabling efficient connectivity across terrestrial, aerial, and maritime domains. The survey then delves into enabling technologies like advanced antenna designs, beamforming techniques, channel modeling, energy consumption models, and mobility optimization, emphasizing their necessity for achieving seamless UAV-to-ground, UAV-to-UAV, and UAV-to-satellite interactions. It further discusses regulatory frameworks and standardization efforts by global entities to address safety, spectrum allocation, and privacy concerns. Innovative routing protocols, including AI-driven and software-defined networking approaches, are analyzed, highlighting their potential to enhance scalability, reduce latency, and optimize resource management in dynamic UAV networks. This work identifies significant challenges such as energy efficiency, secure communication in hostile environments, and trajectory optimization while navigating complex three-dimensional (3D) spaces. The survey finally proposes directions for future research, including the exploration of sub-THz and THz communication, cross-layer routing, and the integration of UAVs with emerging networking paradigms. By synthesizing lessons learned and outlining unresolved questions, this paper serves as a resource for advancing UAV-enabled connectivity and unlocking new capabilities for ubiquitous and resilient wireless networks.</div></div>","PeriodicalId":54346,"journal":{"name":"Vehicular Communications","volume":"56 ","pages":"Article 100977"},"PeriodicalIF":6.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268474","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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