IEEE Open Journal of Vehicular Technology最新文献

{"title":"Cyber Threat Susceptibility Assessment for Heavy-Duty Vehicles Based on ISO/SAE 21434","authors":"Narges Rahimi;Beth-Anne Schuelke-Leech;Mitra Mirhassani","doi":"10.1109/OJVT.2025.3550307","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3550307","url":null,"abstract":"TARA, which stands for Threat Analysis and Risk Assessment, serves as the foundational stage of cybersecurity implementation, particularly in the context of vehicular systems. While various considerations and risk assessment frameworks have been discussed in recent years, there is a notable lack of TARA models specifically designed for heavy-duty (HD) vehicles. The security considerations and vulnerabilities in HD vehicles differ significantly from those in light-duty (LD) vehicles, leading to different security impacts and varying attack feasibility. This makes existing models inadequate for accurately assessing risks in the context of HD vehicles. This study introduces a novel risk assessment model tailored for HD vehicles, addressing gaps in existing TARA frameworks such as EVITA, HEAVENS, and ISO/SAE 21434. The key contribution of this work lies in the customization of impact and feasibility metrics within the ISO/SAE framework to better account for the unique security challenges posed by HD vehicles. Unlike prior models, this approach adapts the impact criteria to reflect the diverse range of security concerns specific to HD vehicles, which have been inadequately addressed in existing frameworks. Additionally, through a comprehensive analysis of threat vectors and vehicle interfaces, the model refines feasibility criteria, ensuring a more accurate and context-aware assessment of security risks. By adopting these enhancements, the proposed model offers more precise risk assessments that align with HD vehicle considerations, helping to prioritize threats and make optimal decisions regarding risk treatment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"969-990"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10921673","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830529","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":"Exploring Graph Neural Backdoors in Vehicular Networks: Fundamentals, Methodologies, Applications, and Future Perspectives","authors":"Xiao Yang;Gaolei Li;Kai Zhou;Jianhua Li;Xingqin Lin;Yuchen Liu","doi":"10.1109/OJVT.2025.3550411","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3550411","url":null,"abstract":"Advances in Graph Neural Networks (GNNs) have substantially enhanced Vehicular Networks (VNs) across primary domains, encompassing traffic forecasting and management, route optimization and algorithmic planning, and cooperative driving. Despite the boosts of the GNN for VNs, recent research has empirically demonstrated its potential vulnerability to backdoor attacks, wherein adversaries integrate triggers into inputs to manipulate GNNs to generate adversary-premeditated malicious outputs (<italic>e.g.</i>, misclassification of vehicle actions or traffic signals). This susceptibility is attributable to adversarial manipulation attacks targeting the training process of GNN-based VN systems. Although there is a rapid increase in research on GNN backdoors, systematic surveys within this domain remain lacking. To bridge this gap, we present the first survey dedicated to GNN backdoors. We start with outlining the fundamental definition of GNNs, followed by the detailed summarization and categorization of current GNN backdoors and countermeasures based on their technical features and application scenarios. Subsequently, an analysis of the applicability paradigms of GNN backdoors is conducted, and prospective research trends are presented. Unlike prior surveys on vision-centric backdoors, we uniquely investigate GNN-oriented backdoor attacks in VNs, which aims to explore attack surfaces across spatiotemporal vehicular graphs and provide insights to security research.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1051-1071"},"PeriodicalIF":5.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10921674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896365","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":"DFT-Spread OFDM-Based MIMO Joint Communication and Sensing System","authors":"Max Schurwanz;Jan Mietzner;Peter Adam Hoeher","doi":"10.1109/OJVT.2025.3549918","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3549918","url":null,"abstract":"This paper introduces a joint communication and sensing (JCAS) system design that employs a discrete Fourier transform (DFT)-spread orthogonal frequency-division multiplexing (OFDM) waveform integrated with a multiple-input multiple-output (MIMO) antenna array. This system has been designed with the specific requirements of future remotely piloted or autonomous aircraft systems in urban air mobility (UAM) settings in mind. The objective is to provide high-bandwidth data transmission in conjunction with precise radar sensing, thereby enhancing situational awareness and facilitating efficient spectrum usage. The paper makes a number of significant contributions to the field, including the development of a flexible MIMO DFT-spread OFDM system model and the introduction of a phase compensation term for comprehensive direction-of-arrival estimation. Additionally, the effects of non-linear power amplifiers on system efficacy are analyzed through detailed simulations, providing a rigorous evaluation of the proposed design's practicality and resilience. The numerical analysis establishes a framework for the design of a JCAS system for UAM, taking into account the influence of realistic electronic components and the respective performance requirements for communication and sensing.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"868-880"},"PeriodicalIF":5.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10919057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792856","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":"Max–Min Secrecy Rate and Secrecy Energy Efficiency Optimization for RIS-Aided VLC Systems: RSMA Versus NOMA","authors":"Omar Maraqa;Sylvester Aboagye;Majid H. Khoshafa;Telex M. N. Ngatched","doi":"10.1109/OJVT.2025.3568436","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3568436","url":null,"abstract":"Integrating visible light communication (VLC) with the reconfigurable intelligent surface (RIS) significantly enhances physical layer security by enabling precise directional signal control and dynamic adaptation to the communication environment. These capabilities strengthen the confidentiality and security of VLC systems. This paper presents a comprehensive study on the joint optimization of VLC access point (AP) power allocation, RIS association, and RIS elements orientation angles for secure VLC systems, while considering rate-splitting multiple access (RSMA) and power-domain non-orthogonal multiple access (NOMA) schemes. Specifically, two frameworks are proposed to maximize both the minimum secrecy rate (SR) and the minimum secrecy energy efficiency (SEE) by jointly optimizing power allocation, RIS association, and RIS elements orientation angles for both power-domain NOMA and RSMA-based VLC systems. The proposed frameworks consider random device orientation and guarantee the minimum user-rate requirement. The proposed optimization frameworks belong to the class of mixed integer nonlinear programming, which has no known feasible solution methodology to guarantee the optimal solution. Moreover, the increased degree of freedom and flexibility from the joint consideration of power control, RIS association and element orientation results in a large set of decision variables and constraints, which further complicates the optimization problem. To that end, we utilize a genetic algorithm-based solution method, which through its exploration and exploitation capabilities can obtain a good quality solution. Additionally, comprehensive simulations show that the RSMA scheme outperforms the power-domain NOMA scheme across both the SR and SEE metrics over various network parameters. Furthermore, useful insights on the impact of minimum user rate requirement, number of RIS elements, and maximum VLC AP transmit power on the minimum SR and SEE performances are provided.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1303-1316"},"PeriodicalIF":5.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10994327","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196911","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":"ISAC Receiver Design: Joint DoA and Data Estimation in the Presence of Incomplete Signal Observations","authors":"Iman Valiulahi;Christos Masouros;Mahmoud Alaaeldin;Emad Alsusa","doi":"10.1109/OJVT.2025.3544148","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3544148","url":null,"abstract":"Integrated sensing and communication (ISAC) receiver design involves the challenge of jointly estimating the communication signal together with the direction of arrivals (DOAs) of the transmitters. This letter proposes an off-the-grid estimator for the ISAC receiver that jointly estimates the DOAs of <inline-formula><tex-math>$K$</tex-math></inline-formula> transmitters together with the communication data. We focus on the challenging case of incomplete observation, i.e., where only a subset of the received signals in space and time are available. We propose a convex optimization based on the dual of atomic norm minimization (ANM). Though the problem is non-deterministic polynomial time (NP)-hard, we leverage the Schur complement technique to develop semidefinite relaxations (SDRs) to implement it. Moreover, we study a fast algorithm based on the alternating direction method of multipliers (ADMM) technique. Finally, our numerical results explore the feasibility of the joint estimation with incomplete observations, while outperforming classical DOA estimators.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"846-852"},"PeriodicalIF":5.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817870","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":"Deep Learning-Based UWB-IMU Data Fusion for Indoor Positioning in Industrial Scenario","authors":"Karthik Muthineni;Alexander Artemenko;Josep Vidal;Montse Nájar;Marisa Catalan;Josep Paradells","doi":"10.1109/OJVT.2025.3566888","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3566888","url":null,"abstract":"Accurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges for wireless signal propagation and affect wireless positioning accuracy due to the interplay of reflected signals from obstacles. The achievable indoor positioning accuracy of the target AGV can be enhanced by fusing the measurements from the wireless infrastructure with the target's onboard sensor data. Nevertheless, the lack of correspondence between the wireless infrastructure and the target's onboard sensors causes the measurements from these two systems to arrive at irregular time steps. Using asynchronous measurements in the data fusion process can degrade the overall positioning accuracy of the target AGV. This paper proposes a novel deep learning-based data fusion approach to deal with asynchronous measurements from the wireless infrastructure Ultra-Wideband (UWB) and the target's onboard Inertial Measurement Unit (IMU) sensor to achieve enhanced positioning accuracy of the target AGV. In particular, a two-stage cascaded Deep Neural Network (DNN) is proposed to deal with the asynchronized measurements from UWB and IMU sensors. The first stage of the DNN is used to obtain the initial position estimate of the AGV by processing the measurements from UWB. Subsequently, the second stage of the DNN fuses the initial position estimate of the AGV with the IMU sensor data to obtain the final enhanced position estimate. The proposed approach is validated with real-world experiments in an indoor industrial scenario using UWB technology in channel 2 (<inline-formula><tex-math>$3.7text{--}4.2,text{GHz}$</tex-math></inline-formula>) and an IMU sensor placed on an AGV. Moreover, the achievable positioning accuracy and the computational runtime to provide the position estimates with the proposed approach are analyzed. The experimental results show that the proposed approach achieves a mean absolute error of less than <inline-formula><tex-math>$text{10},text{cm}$</tex-math></inline-formula>, outperforming the considered baseline methods, Extended Kalman Filter (EKF) and Long Short-Term Memory (LSTM).","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1209-1221"},"PeriodicalIF":5.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10985825","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099970","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":"Adaptive RNN Hyperparameter Tuning for Optimized IDS Across Platforms","authors":"Kamronbek Yusupov;Md Rezanur Islam;Ibrokhim Muminov;Mahdi Sahlabadi;Kangbin Yim","doi":"10.1109/OJVT.2025.3547761","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547761","url":null,"abstract":"Modern vehicles are increasingly vulnerable to cyber-attacks due to the lack of encryption and authentication in the Controller Area Network, which coordinates communication between Electronic Control Units. This study investigates the use of Recurrent Neural Networks to improve the accuracy and efficiency of Intrusion Detection Systems in vehicular networks. Focusing on sequential CAN data, we compare the performance of different RNN architectures, including SimpleRNN, LSTM, and GRU, in detecting common attack types like Denial-of-Service, Fuzzing, Replay, and Malfunction. Sixty-three RNN models were tested with various hyperparameters, including optimizers and learning rates. Our findings indicate that GRU models achieve superior detection performance, particularly in resource-constrained environments, offering near 99% accuracy in identifying cyber threats. The study also explores the implications of six different hardware choices, revealing that devices like Jetson and Raspberry Pi, when paired with optimal hyperparameters, can deliver efficient real-time IDS performance at a lower cost. These results contribute to the ongoing effort to secure vehicular communication systems and highlight the importance of balancing accuracy, resource usage, and system cost in IDS deployment.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"991-1004"},"PeriodicalIF":5.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10909606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845520","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":"Improving SNR for NLoS Target Detection Using Multi-RIS-Assisted Monostatic Radar","authors":"Salman Liaquat;Ijaz Haider Naqvi;Faran Awais Butt;Saleh Alawsh;Nor Muzlifah Mahyuddin;Ali Hussein Muqaibel","doi":"10.1109/OJVT.2025.3547163","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547163","url":null,"abstract":"The use of a reconfigurable intelligent surface (RIS) in radar systems significantly enhances target detection, particularly in challenging non-line-of-sight (NLoS) scenarios. In urban environments, where structures frequently obstruct line-of-sight (LoS) paths, the integration of RISs with existing radars can offer a viable solution for enhancing signal-to-noise ratio (SNR) and improving target detection. Approaches utilizing a single RIS can still fail in scenarios where a link cannot be established. This paper presents a novel approach for deriving a comprehensive expression for the received power, SNR and path loss (PL) in systems where multiple RISs assist a monostatic radar. We analyze the power received in dual RIS configurations and extend this to include additional RISs, demonstrating how each additional RIS placement affects the system's performance. Moreover, the analysis explores the impact of different Swerling target models on the SNR and PL, highlighting the optimal angles for target detection. This multi-RIS strategy offers a substantial performance boost over conventional radars and single RIS-assisted systems, particularly in environments with obstacles. Simulation results demonstrate a significant improvement in SNR with a dual RIS-assisted radar, with up to 14.42 dB gains observed when employing a <inline-formula><tex-math>$46 times 46$</tex-math></inline-formula> element RIS configuration at L-band and 65.47 dB gain when employing a <inline-formula><tex-math>$328 times 328$</tex-math></inline-formula> element RIS configuration at X-band, corresponding to a RIS size of <inline-formula><tex-math>$ 5text{ m} times 5text{ m}$</tex-math></inline-formula> at both frequencies, showing the efficacy of the proposed multi-RIS strategy.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"774-789"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908879","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698350","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":"THz-Enabled UAV Communications Under Pointing Errors: Tractable Statistical Channel Modeling and Security Analysis","authors":"Mohammad Javad Saber;Mazen Hasna;Osamah S. Badarneh","doi":"10.1109/OJVT.2025.3547244","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3547244","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are increasingly being utilized as mobile base stations for rapidly establishing temporary wireless coverage in emergency situations and remote locations. Their high mobility and flexibility make UAVs ideal for quickly deployed communication systems, but these features also introduce unique challenges, particularly in maintaining stable and reliable communication links. The highly directional nature of terahertz (THz) antennas introduces challenges in UAV communication systems. Combined with the mobility of UAVs, this can cause significant issues, such as beam misalignment and signal degradation. Thus, developing accurate radio channel models that address these challenges is critical to ensure reliable communication. In this study, we present an analytical framework focused on evaluating the security performance of highly directional THz-enabled UAV communication links. The challenges analyzed include misalignment of directional beams, path loss, small-scale fading, and UAV-induced vibrations. The small-scale fading is modeled using the <inline-formula><tex-math>$alpha$</tex-math></inline-formula>–<inline-formula><tex-math>$mu$</tex-math></inline-formula> distribution, which accurately represents various fading environments. Using the Meijer G-function, we derive closed-form expressions for key statistical functions, including the probability density function (PDF) and cumulative distribution function (CDF) of the channel gain. Furthermore, a detailed physical-layer security analysis is provided, focusing on metrics such as average secrecy capacity, secrecy outage probability, and the probability of strictly positive secrecy capacity, particularly in the presence of UAV eavesdroppers. Numerical results validate the analytical expressions under different operational conditions, such as beam misalignment and fading, providing valuable insights into the security and performance of THz-enabled UAV communication systems. These results provide important guidelines for optimizing future wireless networks using UAVs and THz frequencies to ensure secure and reliable data transmission in dynamic environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"801-811"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908855","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706650","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 Study on the Impact of Rain on Object Detection for Automotive Applications","authors":"Diarmaid Geever;Tim Brophy;Dara Molloy;Enda Ward;Brian Deegan;Martin Glavin;Edward Jones","doi":"10.1109/OJVT.2025.3566251","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3566251","url":null,"abstract":"Visible spectrum cameras have emerged as a key technology in Advanced Driving Assistance Systems (ADAS) and automated vehicles. An important question to be answered is how these sensors perform in challenging adverse weather conditions, such as rain. Although progress has been made in determining the impact of rain on computer vision performance, previous studies have generally focused on end-to-end object detection system performance and have not addressed the specific impact of rain in detail. Moreover, the lack of image datasets with detailed labeling acquired under rain conditions means that the impact of rain remains a relatively under-researched question. The purpose of this study is to examine the impact of rain in the propagation path on perception tasks, where other factors affecting performance are removed or controlled as far as possible. This study presents the results of controlled experimental testing designed to measure the impact of rain on automated vehicle perception performance. Object detection is performed on the captured data to determine the impact of rain on performance. Four object detection algorithms, a segmentation algorithm, and an optical character recognition algorithm are used as representative examples of typical algorithms used in ADAS. It is shown that the impact of rain varies between models, and at larger distances, rain has a greater impact. In the case of the OCR algorithm, rain is shown to have a larger impact at certain distances. The findings of this study are useful for ADAS design, as they provide more detailed insight into the impact of rain on ADAS and provide guidance on potential breaking points for algorithms typically used in this type of system.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"1287-1302"},"PeriodicalIF":5.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10981986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178484","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}