IEEE Open Journal of Vehicular Technology最新文献

{"title":"A Dynamic Traffic Camouflage Method Based on Adversarial Machine Learning for Connected Vehicles","authors":"Xinyang Bai;Ping Dong;Xiaoya Zhang;Yuyang Zhang;Wenxuan Qiao;Hongke Zhang","doi":"10.1109/OJVT.2026.3666994","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3666994","url":null,"abstract":"While improving the convenience of IoV applications, its communication process faces serious privacy and security threats. Especially in the process of network layer transmission, the analysis of side-channel characteristics (such as packet size and traffic transmission mode) may lead to user privacy leakage. Existing research mostly focuses on website fingerprint camouflage or overall traffic statistical feature camouflage, and usually only designs a camouflage mechanism for a single application layer protocol, which is challenging to achieve unified protection in the multi-protocol scenario of the Internet of Vehicles. This paper proposes LeakGAN Packets Traffic Camouflage (LPTC), which simultaneously supports traffic camouflage for multiple application-layer protocols. The LPTC learns the sending pattern of target traffic packets and generates camouflaged traffic through an adversarial game between a generator and a discriminator, to effectively resist side-channel attacks without affecting communication performance. Experimental results indicate that the proposed LPTC scheme significantly degrades the classification performance of traffic transmission models based on K-Nearest Neighbors, Support Vector Machine, Bagged Trees, and Random Forest algorithms, reducing classification accuracy by 41.23%. In addition, the precision, recall, F1 score, and AUC value decreased by 41.82%, 61.85%, 49.57%, and 42.00%, respectively. It also defends against adaptive attackers who retrain deep learning classifiers on camouflaged traffic. The LPTC substantially enhances the indistinguishability between target and camouflaged traffic, thereby effectively mitigating user privacy leakage caused by side-channel analysis. Meanwhile, the system’s bandwidth and delay performance is sufficient to support the transmission requirements of real-time vehicular networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"887-900"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11406896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Trajectory and RIS Phase Shift Design for RIS-Assisted SAGIN: A FL-MADRL Approach","authors":"Wendenda Nathanael Kabore;Rong-Terng Juang;Hsin-Piao Lin;Ming-Cheng Tsai;Konpal Shaukat Ali;Belayneh Abebe Tesfaw","doi":"10.1109/OJVT.2026.3668772","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3668772","url":null,"abstract":"Wireless systems must deliver ubiquitous QoS for massive IoT, but in urban areas, the blockages severely degrade satellite links. We consider a RIS-enabled space–air–ground integrated network (SAGIN) where a moving LEO satellite provides coverage and multiple drone base stations (DBSs) act as mobile relays to restore connectivity for ground users. Some Reflecting Intelligent Surfaces (RISs) panels, mounted on buildings, create controllable reflect paths to overcome non-line-of-sight conditions. A key challenge is that DBS's 3D trajectory simultaneously affects the satellite–DBS and the DBS-RIS–user access links, while also determining their energy consumption. We formulate a joint optimization problem to maximize the communication coverage, system throughput, and fairness under mobility, energy, and connectivity constraints. To address the issue, we develop a Federated Multi-Agent Deep Reinforcement Learning (FL-MADRL) method that coordinates multiple agents: each agent learns online from local experience, while periodic federated aggregation shares policies without exchanging raw data. Simulations in an urban scenario demonstrate that the proposed approach maintains uninterrupted service in terms of coverage, throughput, and fairness while reducing DBS energy consumption compared to conventional convex approaches and non-federated baselines, indicating its suitability for RIS-assisted SAGIN systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"991-1004"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11415649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DRL-Based RAN Slicing With Efficient Inter-Slice Isolation in Tactical Wireless Networks","authors":"Abderrahime Filali;Diala Naboulsi;Georges Kaddoum","doi":"10.1109/OJVT.2026.3685549","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3685549","url":null,"abstract":"The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and the open RAN (O-RAN) paradigm, to unlock new architectural options for innovative applications. RAN slicing and the O-RAN paradigm are considered game changers in TNs, where the former enables tailoring services to users’ requirements, and the latter brings openness and intelligence to RAN management. In TNs, bandwidth scarcity requires dynamic bandwidth slicing strategies. Although such strategies ensure efficient bandwidth utilization, they may compromise RAN slicing isolation in terms of quality of service (QoS). To deal with this challenge, we propose a deep reinforcement learning (DRL)-based RAN slicing mechanism, termed here as sharing and inter- and intra-slice isolation (BS-IISI), which achieves a trade-off between efficient bandwidth sharing and robust inter- and intra-slice isolation. BS-IISI performs bandwidth allocation in two stages. In the first stage, bandwidth is allocated to RAN slices, while in the second stage, each slice distributes its allocated bandwidth among associated users. In both stages, the slicing operation is constrained by QoS-related considerations that enhance inter- and intra-slice isolation. BS-IISI relies on DRL algorithms to perform bandwidth sharing at each allocation stage. We propose deploying the mechanism within an O-RAN architecture and describe the relevant O-RAN functional blocks and the main phases of the DRL model lifecycle management. Finally, we develop three implementations of the proposed mechanism, each based on a different DRL algorithm, and evaluate their performance against several baselines under diverse network configurations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1263-1278"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11488567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space–Air–Ground-Integrated Networks: The BER vs. Residual Delay and Doppler Analysis","authors":"Chao Zhang;Kunlun Li;Chao Xu;Lie-Liang Yang;Lajos Hanzo","doi":"10.1109/OJVT.2026.3688310","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3688310","url":null,"abstract":"Perfect Doppler compensation and synchronization is nontrivial due to multi-path Doppler effects and Einstein's theory of relativity in the space-air-ground-integrated networks (SAGINs). Hence, by considering the residual Doppler and the synchronization delay, this paper investigates the bit-error-rate (BER) performance attained under time-varying correlated Shadowed-Rician SAGIN channels. First, a practical SAGIN model is harnessed, encompassing correlated Shadowed-Rician channels, the Snell's law-based path loss, atmospheric absorption, the line-of-sight Doppler compensation, elliptical satellite orbits, and Einstein's theory of relativity. Then, a specific correlation coefficient between the pilot and data symbols is derived in the context of correlated Shadowed-Rician Channels. By exploiting this correlation coefficient, the channel distribution is mimicked by a bi-variate Gamma distribution. Then, a closed-form BER formula is derived under employing least-square channel estimation and equalization for 16-QAM. Our analytical results indicate for a 300-km-altitude LEO that 1) the period of realistic elliptical orbits is around 0.8 seconds longer than that of the idealized circular orbits; and 2) the relativistic delay is lower than 1 <inline-formula><tex-math>$mu s$</tex-math></inline-formula> over a full LEO pass (from rise to set). Our numerical results for the L bands quantify the effects of: 1) the residual Doppler; 2) atmospheric shadowing; 3) synchronization errors; and 4) pilot overhead.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1351-1367"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11498355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-UAV-UGV Collaborative Path Planning for Road Network Continuous Monitoring and Emergency Response","authors":"Huihui Yu;Yang Chen;Yanhua Yang;Xingang Zhao","doi":"10.1109/OJVT.2026.3671019","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3671019","url":null,"abstract":"Urban IoT devices face increasing challenges due to urbanization and complex traffic conditions. Traditional monitoring methods are inadequate for tracking urban traffic and ensuring public safety. Collaborative monitoring models involving multiple Uncrewed Aerial Vehicles (UAVs) and Uncrewed Ground Vehicles (UGVs) are emerging. However, sudden emergencies such as traffic accidents and geological disasters can disrupt monitoring tasks across urban traffic networks, leading to traffic paralysis and personal injury. This study develops a mathematical model for multi-UAV-UGV path planning, specifically tailored for dual mission applications, namely continuous monitoring and emergency response. A foresight-based task allocation and path optimization algorithm is proposed, which accounts for future situation prediction. This algorithm takes into account various constraints, such as the road network restriction and information timeliness, with the aim of optimizing system efficiency and response speed. Various factors are comprehensively considered to select response vehicles, and the potential loss caused by emergency situations of the entire road network is reduced in the case of rapid emergency treatment. Simulation results demonstrate that this method is capable of providing an efficient and reliable path for dual mission operation. Additionally, the algorithm exhibits good stability and strong adaptability to random emergency events.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"946-960"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Low-Cost and Accurate Train Localization Method Based on Vision-Aided IMU Calibration","authors":"Kensuke Nagai;Wataru Ohnishi;Takafumi Koseki;Shota Miyoshi;Tomonori Hasegawa","doi":"10.1109/OJVT.2026.3711628","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3711628","url":null,"abstract":"Accurate train localization is crucial for collision avoidance. However, the high costs of ground-based infrastructure, such as balises and track circuits, burden railway operators. In this paper, we propose a low-cost and accurate train localization method that uses measurements from inexpensive onboard sensors and image recognition. By combining trackside object detection via a monocular camera with a track map, we compensate for the gravitational influence inherent in acceleration measurements. Furthermore, by acquiring the train’s absolute position through landmark detection, we calibrate the inevitable accumulated errors in acceleration and velocity integration. Experimental results from a commercial railway line in Japan demonstrate that our method achieves a localization accuracy of approximately 5 meters in Root Mean Square Error using only low-cost sensors, without relying on GNSS, even in rainy conditions. In summary, our proposed localization approach can be deployed in GNSS-denied environments as well as on low-budget regional lines, thereby contributing to the sustainable operation of railway systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2122-2132"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11603905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health-Aware Energy Management of Energy Storage Systems in Modular Power Converters","authors":"Alaa Hijazi;Robert Alfie S. Peña;Pascal Venet","doi":"10.1109/OJVT.2026.3711491","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3711491","url":null,"abstract":"Supercapacitors, characterized by high power density and relatively long cycle life, are mainly used for high-power and transient applications. However, their lifespan can be significantly affected by loading and temperature factors. This article investigates the use of optimized control of modular power converters to enhance the lifetime of supercapacitor energy storage systems (SESS). The effectiveness of the developed methods is demonstrated through real-world applications in trolleybus SESS auxiliary power supply and modular multilevel converter (MMC) with integrated SESS. Supercapacitor ageing models are employed to capture the degradation of supercapacitor performance over time. The control allocation method adapted for the SESS connected to modular power converters considers supercapacitor lifetime to optimize the SESS availability. It is implemented to distribute the load among the converters in a way that loads healthier supercapacitors more than the weaker ones, maximizing overall SESS health. The modular converter approach was found to extend the lifetime of the SESS by more than 44% when applied to the trolleybus SESS auxiliary supply and by more than 10 % when applied to the MMC with integrated SESS, The second application focuses on a Modular Multilevel Converter incorporating an integrated Energy Storage System, aimed at enhancing dynamic energy management capabilities, mitigating power fluctuations, and improving the overall stability and performance of the electrical grid.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"2243-2254"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11599632","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148626238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Inter-Modality Misalignment on Multispectral Object Detection","authors":"Muhammad Usama;Imad Ali Shah;Roshan George;Enda Ward;Fiachra Collins;William O’Grady;Martin Glavin;Brian Deegan;Edward Jones","doi":"10.1109/OJVT.2026.3673912","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3673912","url":null,"abstract":"Object Detection significantly benefits from combining complementary spectral modalities; however, its performance suffers greatly from misregistration between sensor streams due to spatial misalignments, viewpoint differences between sensors, and inconsistent annotations, a common feature of multi-modal datasets used in object detection system development. In this work, we address the critical challenge of modality misregistration. We propose a comprehensive alignment framework based on object-aware local homography estimation, anchored on Long-Wave Infrared Radiation (LWIR) annotations, to generate a spatially aligned training dataset for improved model development. For our experiments, we utilise the Multispectral Object Detection (MOD) dataset, which includes RGB, Short-Wave Infrared Radiation (SWIR), Mid-Wave Infrared Radiation (MWIR), and Long-Wave Infrared Radiation (LWIR) imagery. The proposed alignment pipeline ensures a consistent field of view, resolution normalization, and accurate cross-modality annotation projection. To quantify the impact of alignment, we benchmark four state-of-the-art fusion architectures (DEYOLO, TarDAL, CMTFusion, and U2Fusion) on both the original and the aligned versions of the MOD dataset. Experimental results demonstrate significant improvements of 10 to 30 percentage points in mAP@0.5 after training on the aligned MOD data, with enhanced localization accuracy and reduced false detections. We further verified the generalizability of the proposed alignment framework on a different dataset (FLIR ADAS), observing 3.54 percentage points improvement in mAP@0.5 after training on the aligned RGB-LWIR image pairs. U2Fusion emerged as the architecture most tolerant to misregistration in initial experiments, and this architecture is used to conduct a further sensitivity analysis to evaluate model robustness against real-world misalignments, in a controlled way. The results of this sensitivity analysis could be useful for designers of practical systems, where small misalignment errors between image modalities commonly occur.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1110-1123"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11435664","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Vehicular Technology Society IEEE Open Journal on Vehicular Technology Information","authors":"","doi":"10.1109/OJVT.2025.3646500","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3646500","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"C4-C4"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11360986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148486150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Learning-Assisted Compressive Sensing Improves Multi-Target Parameter Estimation in 5G-NR Inspired ISAC","authors":"Prabhanshu Yadav;Kunal Kumar Singh;Suraj Srivastava;Lajos Hanzo","doi":"10.1109/OJVT.2026.3697606","DOIUrl":"https://doi.org/10.1109/OJVT.2026.3697606","url":null,"abstract":"Integrated sensing and communication (ISAC) has emerged as a key technology for 6G communication systems, enabling both spectrum and hardware sharing between radar and communication systems. In this paper, we present a 5G New Radio (5G-NR) inspired ISAC system that enhances sensing accuracy and robustness by smartly combining various standard reference symbol outputs in a multi-target scenario. Toward this, we first formulate a novel multiple measurement vector (MMV)-based sparse signal recovery (SSR)-aided compressive sensing (CS) problem, which employs multiple subcarriers and symbols in the time-frequency grid of 5G-NR for improved velocity and range profile estimation. Next we propose an MMV Bayesian learning (BL) algorithm, which leverages the simultaneous-sparse structure of velocity/range profiles for enhanced profile estimation from compressed pilot outputs. Our analysis is supported by extensive simulation results confirming that the proposed technique outperforms the existing state-of-the-art algorithms, in the most critical low signal-to-noise ratio (SNR) regime.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"7 ","pages":"1576-1586"},"PeriodicalIF":6.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11535746","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}