IEEE Open Journal of Vehicular Technology最新文献

{"title":"IEEE Vehicular Technology Society IEEE Open Journal on Vehicular Technology Information","authors":"","doi":"10.1109/OJVT.2024.3490479","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3490479","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10812005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875084","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":"Editorial: Message From the Editor-in-Chief","authors":"Edward Au","doi":"10.1109/OJVT.2024.3512052","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3512052","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"viii-viii"},"PeriodicalIF":5.3,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10803008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825779","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 Open Journal of Vehicular Technology Information for Authors","authors":"","doi":"10.1109/OJVT.2024.3490477","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3490477","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10795781","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810414","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":"LA-DETECTS: Local and Adaptive Data-Centric Misbehavior Detection Framework for Vehicular Technology Security","authors":"Rukhsar Sultana;Jyoti Grover;Meenakshi Tripathi;Prinkle Sharma","doi":"10.1109/OJVT.2024.3513152","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3513152","url":null,"abstract":"Vehicular Ad Hoc Networks (VANET) represent an immense technological advancement enhancing connectivity among Vehicular Technology including vehicles and roadside infrastructure to ensure road safety and improve forthcoming transportation services. The effectiveness of safety applications depends on the reliability and consistency of periodically broadcasted real-time environmental and vehicle state information. However, insider threats arise when nodes with valid access credentials disseminate maliciously incorrect information. Existing misbehavior detection solutions are often static and lack the adaptability required for the dynamic nature of vehicular networks, leaving a gap in addressing sophisticated attacks such as Denial of Service (DoS), data replay, and Sybil attacks. To fill this gap, we propose a context-aware, data-driven misbehavior detection framework that allows each vehicle to perform plausibility and consistency checks on received messages. The Adaptive Misbehavior Detection Framework addresses critical security challenges within localized vehicles by incorporating dynamically computed parameters and confidence intervals to assess message integrity. To determine the presence of misbehavior, a weighted average approach effectively reduces the possibility of false positives. Simulation results demonstrate that our proposed mechanism significantly enhances detection performance against key misbehavior types, including false information dissemination, DoS, disruptive, and variants of Sybil attacks variants, outperforming existing benchmarks with the VeReMi extension dataset.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"145-169"},"PeriodicalIF":5.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10782992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858977","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":"Optimal Linear Precoding Under Realistic Satellite Communications Scenarios","authors":"Geoffrey Eappen;Jorge Luis Gonzalez;Vibhum Singh;Rakesh Palisetty;Alireza Haqiqtnejad;Liz Martinez Marrero;Jevgenij Krivochiza;Jorge Querol;Nicola Maturo;Juan Carlos Merlano Duncan;Eva Lagunas;Stefano Andrenacci;Symeon Chatzinotas","doi":"10.1109/OJVT.2024.3509646","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3509646","url":null,"abstract":"In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16 × 16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB-S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"81-91"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825893","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":"Optimizing Small Cell Performance: A New MIMO Paradigm With Distributed ASTAR-RISs","authors":"Shakil Ahmed;Ahmed E. Kamal;Mohamed Y. Selim;Md Akbar Hossain;Saifur Rahman Sabuj","doi":"10.1109/OJVT.2024.3509736","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3509736","url":null,"abstract":"As the demand for high-speed data transmission grows with the expected emergence of 6G networks and the proliferation of wireless devices, more than traditional wireless infrastructure may be required. Small cell networks (ScNs) integrated with reconfigurable intelligent surfaces (RISs) and multiple-inputmultiple-output (MIMO) have emerged as promising solutions to address this issue. However, ScNs have resource allocation limitations, and traditional RISs can only reflect signals in a limited propagation space of 1800 with fixed reflection properties. This paper proposes a novel approach to overcome these challenges by introducing actively simultaneously transmitting and reflecting (ASTAR)-RISs. Unlike conventional RIS, ASTAR-RISs actively amplify and transmit signals, effectively mitigating the limited propagation challenge and improving signal strength, especially in dense ScNs. This approach enhances the quality of service in complex channel environments by amplifying, on top of reflection, from the macro base station (mBS), improving the overall signal strength, and providing 3600 flexible propagation space. Furthermore, ASTAR-RIS enables dynamic beam management, significantly improving signal coverage and interference management, which are crucial in dense deployments. In this work, we propose a network architecture where distributed ASTAR-RIS units are deployed to assist small cell mBSs by optimizing signal coverage and enhancing communication performance. ASTAR-RISs dynamically control signal reflection and amplification, complementing the functionality of traditional small-cell BSs in dense network environments. Using the MIMO technique, we design phase shifts for ASTAR elements and develop optimal hybrid beamforming for users at the mBS. We dynamically control the ON/OFF status of the ASTAR-RIS based on active or idle status. We propose an efficient model that ensures fairness of signal-to-noise ratio (SNR) for all users and minimizes overall power consumption while meeting user SNR and phase shift constraints. To this end, we integrate robust beamforming and power allocation strategies, ensuring the system maintains reliable performance even under imperfect channel state information (CSI). We formulate a max-min optimization problem that optimizes the SNR and power consumption, subject to the ON/OFF status, phase shift, and power budget of the ASTAR-RIS. Our proposed method uses an alternating optimization algorithm to optimize the phase shift matrix at the ASTAR-RIS and the hybrid beamforming at the mBS. The approach includes two transmission schemes, and the phase optimization problem is solved using a successive convex approximation method that offers a closed-form solution at each step. Additionally, we use the dual method to determine the optimal ON/OFF status of the ASTAR-RIS. Comprehensive simulations validate the robustness and scalability of our proposed solution, particularly under varying network densities and CSI uncertaint","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"128-144"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859068","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":"Conditioned Adaptive Barrier Function Based Integral Super-Twisting Sliding Mode Control for Electric Vehicles With Hybrid Energy Storage System","authors":"Afaq Ahmed;Iftikhar Ahmad;Habibur Rehman;Ammar Hasan","doi":"10.1109/OJVT.2024.3509686","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3509686","url":null,"abstract":"This paper proposes a conditioned adaptive barrier function-based integral super-twisting sliding mode controller for the hybrid energy storage system (HESS) with a field-oriented control of 3-phase induction motor for the electric vehicles (EVs). The conditioned approach ensures that the control input stays within bounds, the adaptive barrier adjusts the sliding mode controller (SMC) gains, and the super-twisting technique helps in reducing the chattering. Consequently, the overall system performance is improved. The HESS consists of a fuel cell, battery, and super-capacitor. A rule-based energy management system has been designed, defining different modes of operation for an efficient use of energy sources under different loading conditions. The designed energy management system accounts for the power inflow and the status of the energy sources. The proposed controller ensures smooth energy sources current tracking and stabilizes the DC bus voltage while controlling the motor speed and flux under various operating conditions. The controller's global asymptotic stability has been verified through Lyapunov stability analysis. Intensive computer simulations using Matlab/Simulink are performed to validate the proposed controller's performance and compare it with the conventional PI and SMC controllers. Finally, controller hardware-in-the-loop validation has been conducted for the real-time performance validation.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"92-108"},"PeriodicalIF":5.3,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844588","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":"Unified 3D Networks: Architecture, Challenges, Recent Results, and Future Opportunities","authors":"Mohamed Rihan;Dirk Wübben;Abhipshito Bhattacharya;Marina Petrova;Xiaopeng Yuan;Anke Schmeink;Amina Fellan;Shreya Tayade;Mervat Zarour;Daniel Lindenschmitt;Hans Schotten;Armin Dekorsy","doi":"10.1109/OJVT.2024.3508026","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3508026","url":null,"abstract":"The very new evolution towards 6G networks necessitates a paradigm shift towards unified 3D network architectures, encompassing space, air, and ground segments. This paper outlines the conceptualization, challenges, and prospects of such a transformative architecture. We outline the foundational principles, drawn from standardization endeavors and cutting-edge research initiatives, to articulate the envisioned architecture poised to redefine network capabilities. Driven by the need to enhance capacity, increase data rates, support diverse mobility models, and facilitate heterogeneous connectivity, the conceptual framework of a unified 3D network is presented. The focus is on seamlessly integrating diverse network segments and fostering holistic network orchestration. In examining the technical challenges inherent to the realization of a unified 3D network, we outline our strategies to address mobility management, handover optimization, interference mitigation, and the integration of distributed physical layer concepts. Proposals encompass federated learning mechanisms, advanced beamforming techniques, and energy-efficient computational offloading strategies, aimed at enhancing network performance and resilience. Moreover, we outline compelling utilization scenarios and highlighted promising avenues for future research.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"170-201"},"PeriodicalIF":5.3,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10770553","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858978","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":"Harmonics Measurement, Analysis, and Impact Assessment of Electric Vehicle Smart Charging","authors":"Murat Senol;I. Safak Bayram;Lewis Hunter;Kristian Sevdari;Connor McGarry;David Campos Gaona;Oliver Gehrke;Stuart Galloway","doi":"10.1109/OJVT.2024.3505778","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3505778","url":null,"abstract":"Smart charging for Electric Vehicles (EVs) is gaining traction as a key solution to alleviate grid congestion, delay the need for costly network upgrades, and capitalize on off-peak electricity rates. Governments are now enforcing the inclusion of smart charging capabilities in EV charging stations to facilitate this transition. While much of the current research focuses on managing voltage profiles, there is a growing need to examine harmonic emissions in greater detail. This study presents comprehensive data on harmonic distortion during the smart charging of eight popular EV models. We conducted an experimental analysis, measuring harmonic levels with charging current increments of 1A, ranging from the minimum to the maximum for each vehicle. The analysis compared harmonic emissions from both single and multiple EV charging scenarios against the thresholds for total harmonic distortion (THD) and individual harmonic limits outlined in power quality standards (e.g. IEC). Monte Carlo simulations were employed to further understand the behavior in multi-vehicle scenarios. The results reveal that harmonic distortion increases as the charging current decreases across both single and multiple vehicle charging instances. In case studies where several vehicles charge simultaneously, the findings show that as more EVs charge together, harmonic cancellation effects become more pronounced, leading to a gradual reduction in overall harmonic distortion. However, under worst-case conditions, the aggregate current THD can rise as high as 25%, with half of the tested vehicles surpassing the individual harmonic limits.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"109-127"},"PeriodicalIF":5.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766666","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142875114","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":"N-DriverMotion: Driver Motion Learning and Prediction Using an Event-Based Camera and Directly Trained Spiking Neural Networks on Loihi 2","authors":"Hyo Jong Chung;Byungkon Kang;Yoon Seok Yang","doi":"10.1109/OJVT.2024.3504481","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3504481","url":null,"abstract":"Driver motion recognition is a key factor in ensuring the safety of driving systems. This paper presents a novel system for learning and predicting driver motions, along with an event-based (720 × 720) dataset, N-DriverMotion, newly collected to train a neuromorphic vision system. The system includes an event-based camera that generates a driver motion dataset representing spike inputs and efficient spiking neural networks (SNNs) that are effective in training and predicting the driver's gestures. The event dataset consists of 13 driver motion categories classified by direction (front, side), illumination (bright, moderate, dark), and participant. A novel optimized four-layer convolutional spiking neural network (CSNN) was trained directly without any time-consuming preprocessing. This enables efficient adaptation to energy- and resource-constrained on-device SNNs for real-time inference on high-resolution event-based streams. Compared to recent gesture recognition systems adopting neural networks for vision processing, the proposed neuromorphic vision system achieves competitive accuracy of 94.04% in a 13-class classification task, and 97.24% in an unexpected abnormal driver motion classification task with the CSNN architecture. Additionally, when deployed to Intel Loihi 2 neuromorphic chips, the energy-delay product (EDP) of the model achieved 20,721 times more efficient than that of a non-edge GPU, and 541 times more efficient than edge-purpose GPU. Our proposed CSNN and the dataset can be used to develop safer and more efficient driver-monitoring systems for autonomous vehicles or edge devices requiring an efficient neural network architecture.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"68-80"},"PeriodicalIF":5.3,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10763457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810415","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}