Pub Date : 2025-09-18DOI: 10.1109/OJVT.2025.3611507
Zhipeng Wang;Soon Xin Ng;Mohammed El-Hajjar
In the past decade, unmanned aerial vehicles (UAVs) technology has developed rapidly, while the flexibility and low cost of UAVs make them attractive in many applications. Path planning for UAVs is crucial in most applications, where the path planning for UAVs in unknown, while complex 3D environments has also become an urgent challenge to mitigate. In this paper, we consider the unknown 3D environment as a partially observable Markov decision process (POMDP) problem and we derive the Bellman equation without the introduction of belief state (BS) distribution. More explicitly, we use an independent emulator to model the environmental observation history, and obtain an approximate BS distribution of the state through Monte Carlo simulation in the emulator, which eliminates the need for BS calculation to improve training efficiency and path planning performance. Additionally, we propose a three-dimensional spatial information compression (3DSIC) algorithm to continuous POMDP environment that can compress 3D environmental information into 2D, greatly reducing the search space of the path planning algorithms. The simulation results show that our proposed 3D spatial information compression based deep deterministic policy gradient (3DSIC-DDPG) algorithm can improve the training efficiency by 95.9% compared to the traditional DDPG algorithm in unknown 3D environments. Additionally, the efficiency of combining 3DSIC with fast recurrent stochastic value gradient (FRSVG) algorithm, which can be considered as the most advanced state-of-the-art planning algorithm for the UAV, is 95% higher than that of FRSVG without 3DSIC algorithm in unknown environments.
{"title":"3D Spatial Information Compression Based Deep Reinforcement Learning for UAV Path Planning in Unknown Environments","authors":"Zhipeng Wang;Soon Xin Ng;Mohammed El-Hajjar","doi":"10.1109/OJVT.2025.3611507","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3611507","url":null,"abstract":"In the past decade, unmanned aerial vehicles (UAVs) technology has developed rapidly, while the flexibility and low cost of UAVs make them attractive in many applications. Path planning for UAVs is crucial in most applications, where the path planning for UAVs in unknown, while complex 3D environments has also become an urgent challenge to mitigate. In this paper, we consider the unknown 3D environment as a partially observable Markov decision process (POMDP) problem and we derive the Bellman equation without the introduction of belief state (BS) distribution. More explicitly, we use an independent emulator to model the environmental observation history, and obtain an approximate BS distribution of the state through Monte Carlo simulation in the emulator, which eliminates the need for BS calculation to improve training efficiency and path planning performance. Additionally, we propose a three-dimensional spatial information compression (3DSIC) algorithm to continuous POMDP environment that can compress 3D environmental information into 2D, greatly reducing the search space of the path planning algorithms. The simulation results show that our proposed 3D spatial information compression based deep deterministic policy gradient (3DSIC-DDPG) algorithm can improve the training efficiency by 95.9% compared to the traditional DDPG algorithm in unknown 3D environments. Additionally, the efficiency of combining 3DSIC with fast recurrent stochastic value gradient (FRSVG) algorithm, which can be considered as the most advanced state-of-the-art planning algorithm for the UAV, is 95% higher than that of FRSVG without 3DSIC algorithm in unknown environments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2662-2676"},"PeriodicalIF":4.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11170407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255883","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}
Intelligent control of Uncrewed Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in black-boxed communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. However, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. To address these limitations, we propose a Role-Adaptive LLM-Driven Yoked navigation algorithm RALLY. Specifically, we first develop an LLM-driven semantic decision framework that uses structured natural language for efficient semantic communication and collaborative reasoning. Afterward, we introduce a dynamic role-heterogeneity mechanism for adaptive role switching and personalized decision-making. Furthermore, we propose a Role-value Mixing Network (RMIX)-based assignment strategy that integrates LLM offline priors with MARL online policies to enable offline training of role selection strategies. Experiments in the Multi-Agent Particle Environment (MPE) and a Software-In-The-Loop (SITL) platform demonstrate that RALLY outperforms conventional approaches in terms of task coverage, convergence speed, and generalization, highlighting its strong potential for collaborative navigation in agentic multi-UAV systems.
{"title":"RALLY: Role-Adaptive LLM-Driven Yoked Navigation for Agentic UAV Swarms","authors":"Ziyao Wang;Rongpeng Li;Sizhao Li;Yuming Xiang;Haiping Wang;Zhifeng Zhao;Honggang Zhang","doi":"10.1109/OJVT.2025.3610852","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3610852","url":null,"abstract":"Intelligent control of Uncrewed Aerial Vehicles (UAVs) swarms has emerged as a critical research focus, and it typically requires the swarm to navigate effectively while avoiding obstacles and achieving continuous coverage over multiple mission targets. Although traditional Multi-Agent Reinforcement Learning (MARL) approaches offer dynamic adaptability, they are hindered by the semantic gap in black-boxed communication and the rigidity of homogeneous role structures, resulting in poor generalization and limited task scalability. Recent advances in Large Language Model (LLM)-based control frameworks demonstrate strong semantic reasoning capabilities by leveraging extensive prior knowledge. However, due to the lack of online learning and over-reliance on static priors, these works often struggle with effective exploration, leading to reduced individual potential and overall system performance. To address these limitations, we propose a Role-Adaptive LLM-Driven Yoked navigation algorithm RALLY. Specifically, we first develop an LLM-driven semantic decision framework that uses structured natural language for efficient semantic communication and collaborative reasoning. Afterward, we introduce a dynamic role-heterogeneity mechanism for adaptive role switching and personalized decision-making. Furthermore, we propose a Role-value Mixing Network (RMIX)-based assignment strategy that integrates LLM offline priors with MARL online policies to enable offline training of role selection strategies. Experiments in the Multi-Agent Particle Environment (MPE) and a Software-In-The-Loop (SITL) platform demonstrate that RALLY outperforms conventional approaches in terms of task coverage, convergence speed, and generalization, highlighting its strong potential for collaborative navigation in agentic multi-UAV systems.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2693-2708"},"PeriodicalIF":4.8,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11168174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255932","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}
Pub Date : 2025-09-16DOI: 10.1109/OJVT.2025.3610180
Ricardo Serras Santos;Tiago Brogueira;Slavisa Tomic;João P. Matos-Carvalho;Marko Beko
This work addresses the problem of autonomous target navigation in indoor environments through wireless sensing. To accomplish accurate navigation, it proposes a novel yet simple localization algorithm based on basic geometry and Weighted Central Mass (WCM) by extracting range measurements from received wireless signals. To avoid obstacle collision in the considered indoor environments, the work proposes a new obstacle detection scheme that is based on wireless sensing, where abrupt signal fluctuations throughout the target's movement are exploited to detect and avoid obstructions. Therefore, integrating the two proposed solutions allows for partially autonomous target navigation in indoor environments without resorting to expensive and complex hardware, such as LiDARs or cameras. The proposed solutions are validated through both simulation and experimental test beds, that corroborate their effectiveness, both in terms of navigation and obstacle detection accuracy.
{"title":"Toward Autonomous Target Navigation in Indoor Environments via Wireless Sensing","authors":"Ricardo Serras Santos;Tiago Brogueira;Slavisa Tomic;João P. Matos-Carvalho;Marko Beko","doi":"10.1109/OJVT.2025.3610180","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3610180","url":null,"abstract":"This work addresses the problem of autonomous target navigation in indoor environments through wireless sensing. To accomplish accurate navigation, it proposes a novel yet simple localization algorithm based on basic geometry and Weighted Central Mass (WCM) by extracting range measurements from received wireless signals. To avoid obstacle collision in the considered indoor environments, the work proposes a new obstacle detection scheme that is based on wireless sensing, where abrupt signal fluctuations throughout the target's movement are exploited to detect and avoid obstructions. Therefore, integrating the two proposed solutions allows for partially autonomous target navigation in indoor environments without resorting to expensive and complex hardware, such as LiDARs or cameras. The proposed solutions are validated through both simulation and experimental test beds, that corroborate their effectiveness, both in terms of navigation and obstacle detection accuracy.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2627-2641"},"PeriodicalIF":4.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210154","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}
With the increasing use of frequency modulated continuous wave (FMCW) radars in autonomous vehicles, mutual interference among FMCW radars poses a serious challenge. In this work, we present a novel approach to effectively and elegantly suppress mutual interference in FMCW radars. We first decompose the received signal into modes using variational mode decomposition (VMD) and perform time-frequency analysis using Fourier synchrosqueezed transform (FSST). The interference-suppressed signal is then reconstructed by applying a proposed energy-entropy-based thresholding operation on the time-frequency spectra of the VMD modes. The effectiveness of the proposed method is measured in terms of signal-to-interference plus noise ratio (SINR), correlation coefficient, and probability of detection in the presence of FMCW interference. Furthermore, the interference suppression ability of the proposed VAFER scheme is evaluated for stationary and moving target scenarios by performing a range Doppler analysis in the presence of interference. Compared to the existing literature, the proposed method demonstrates significant improvement in the output SINR by at least 15.46 dB for simulated data and 9.87 dB for experimental data.
{"title":"Signal Decomposition Based Mutual Interference Suppression in FMCW Radars","authors":"Abhilash Gaur;Po-Hsuan Tseng;Kai-Ten Feng;Seshan Srirangarajan","doi":"10.1109/OJVT.2025.3610715","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3610715","url":null,"abstract":"With the increasing use of frequency modulated continuous wave (FMCW) radars in autonomous vehicles, mutual interference among FMCW radars poses a serious challenge. In this work, we present a novel approach to effectively and elegantly suppress mutual interference in FMCW radars. We first decompose the received signal into modes using variational mode decomposition (VMD) and perform time-frequency analysis using Fourier synchrosqueezed transform (FSST). The interference-suppressed signal is then reconstructed by applying a proposed energy-entropy-based thresholding operation on the time-frequency spectra of the VMD modes. The effectiveness of the proposed method is measured in terms of signal-to-interference plus noise ratio (SINR), correlation coefficient, and probability of detection in the presence of FMCW interference. Furthermore, the interference suppression ability of the proposed VAFER scheme is evaluated for stationary and moving target scenarios by performing a range Doppler analysis in the presence of interference. Compared to the existing literature, the proposed method demonstrates significant improvement in the output SINR by at least 15.46 dB for simulated data and 9.87 dB for experimental data.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2677-2692"},"PeriodicalIF":4.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255934","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}
Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising solution to enhance the security and reliability of wireless communication systems by intelligently reshaping the propagation environment. Although conventional passive RIS improves signal's strength through phase-shift control, its inability to amplify signals limits the overall system's performance. This limitation is addressed by the active RIS, which integrates amplification capabilities to offer significant performance enhancements. With the help of novel recursive integrals, this paper presents accurate yet analytically tractable closed-form solutions for the outage probability (OP) and the secrecy outage probability (SOP) for active RIS-aided wireless communication systems under Nakagami-$m$ fading conditions. To achieve the same level of target reliability of 0.4, we demonstrate that under certain degrees of the fading severity, and at some constant value of the receiver's signal-to-noise ratio (SNR), an active RIS-aided structure with amplification gain of 10 dB help in reducing the required number of reflecting elements by nearly 90% compared to its passive counterpart. This underscores the practical and economic advantages of active RIS in terms of reduced hardware complexity and deployment cost. The number of elements needed can be further reduced by increasing the amplification gain of the active reflecting elements. Additionally, the asymptotic receiver SNR analysis is carried out which further provides an insight into the advantages of incorporating active reflecting elements into the system's design as compared to the corresponding passive elements. Precisely, for the same number of elements, the active RIS-aided systems achieve a considerable user's SNR of 40 dB as compared to the passive RIS-aided systems; for all values of the Nakagami-$m$ fading parameters. All the analytical results are validated through extensive Monte-Carlo simulations.
{"title":"Performance Analysis of Active RIS-Aided Wireless Communication Systems Over Nakagami-$m$ Fading Channel","authors":"Leuva Bhumika Ranchhodbhai;Dharmendra Sadhwani;Rachna Singh","doi":"10.1109/OJVT.2025.3609899","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3609899","url":null,"abstract":"Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising solution to enhance the security and reliability of wireless communication systems by intelligently reshaping the propagation environment. Although conventional passive RIS improves signal's strength through phase-shift control, its inability to amplify signals limits the overall system's performance. This limitation is addressed by the active RIS, which integrates amplification capabilities to offer significant performance enhancements. With the help of novel recursive integrals, this paper presents accurate yet analytically tractable closed-form solutions for the outage probability (OP) and the secrecy outage probability (SOP) for active RIS-aided wireless communication systems under Nakagami-<inline-formula><tex-math>$m$</tex-math></inline-formula> fading conditions. To achieve the same level of target reliability of 0.4, we demonstrate that under certain degrees of the fading severity, and at some constant value of the receiver's signal-to-noise ratio (SNR), an active RIS-aided structure with amplification gain of 10 dB help in reducing the required number of reflecting elements by nearly 90% compared to its passive counterpart. This underscores the practical and economic advantages of active RIS in terms of reduced hardware complexity and deployment cost. The number of elements needed can be further reduced by increasing the amplification gain of the active reflecting elements. Additionally, the asymptotic receiver SNR analysis is carried out which further provides an insight into the advantages of incorporating active reflecting elements into the system's design as compared to the corresponding passive elements. Precisely, for the same number of elements, the active RIS-aided systems achieve a considerable user's SNR of 40 dB as compared to the passive RIS-aided systems; for all values of the Nakagami-<inline-formula><tex-math>$m$</tex-math></inline-formula> fading parameters. All the analytical results are validated through extensive Monte-Carlo simulations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2539-2553"},"PeriodicalIF":4.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11164369","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210153","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}
With the advancement of Beyond 5G/6G technologies, accurate positioning and velocity estimation in Internet of Vehicles (IoV) systems has become increasingly critical. Although GPS can provide real-time location information, its performance degrades significantly in environments with heavy obstructions, such as urban areas surrounded by skyscrapers. To address this limitation, this study proposes a positioning framework that relies on channel parameter estimation derived from multi-antenna signal processing. Specifically, we adopt an adaptive low-complexity 2D MUSIC (ALC2D-MUSIC) algorithm to estimate signal directions, and further apply an unscented Kalman filter (UKF) using extracted Direction of Arrival (DoA) and Time of Arrival (ToA) information to estimate vehicle positions and velocities. The proposed system is robust to variations in road geometry, making it suitable for deployment in diverse traffic environments. Simulation results demonstrate that our method achieves high estimation accuracy and outperforms a compressive sensing-based approach across different SNR levels, angular search resolutions, and antenna array sizes. Furthermore, the UKF-based tracking algorithm shows superior performance in curved road scenarios, validating its effectiveness under realistic mobility conditions.
{"title":"DoA Estimation and Kalman Filter Based Multi-Antenna System for Vehicle Position in mmWave Network","authors":"Dou Hu;Jin Nakazato;Javanmardi Ehsan;Kazuki Maruta;Rui Dinis;Manabu Tsukada","doi":"10.1109/OJVT.2025.3608747","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3608747","url":null,"abstract":"With the advancement of Beyond 5G/6G technologies, accurate positioning and velocity estimation in Internet of Vehicles (IoV) systems has become increasingly critical. Although GPS can provide real-time location information, its performance degrades significantly in environments with heavy obstructions, such as urban areas surrounded by skyscrapers. To address this limitation, this study proposes a positioning framework that relies on channel parameter estimation derived from multi-antenna signal processing. Specifically, we adopt an adaptive low-complexity 2D MUSIC (ALC2D-MUSIC) algorithm to estimate signal directions, and further apply an unscented Kalman filter (UKF) using extracted Direction of Arrival (DoA) and Time of Arrival (ToA) information to estimate vehicle positions and velocities. The proposed system is robust to variations in road geometry, making it suitable for deployment in diverse traffic environments. Simulation results demonstrate that our method achieves high estimation accuracy and outperforms a compressive sensing-based approach across different SNR levels, angular search resolutions, and antenna array sizes. Furthermore, the UKF-based tracking algorithm shows superior performance in curved road scenarios, validating its effectiveness under realistic mobility conditions.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2761-2775"},"PeriodicalIF":4.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11162567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145351906","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}
Industrial Vehicle-to-Infrastructure (iV2I) networks are increasingly adopted in settings such as warehouses, construction sites, and smart factories to enhance automation and operational efficiency. However, these systems face growing cybersecurity risks that threaten safety-critical operations. This paper introduces a realistic synthetic dataset created using the ID2T framework, which injects malicious traffic, such as DDoS, PortScan, and memory corruption exploits, into benign communication traces collected from actual iV2I environments. The resulting hybrid dataset, combining synthetic and real-world traffic, enables the supervised training of a Multi-Layer Perceptron (MLP) neural network using 16 meticulously crafted flow-based features. Experimental results demonstrate high detection accuracy under both balanced and threat-specific conditions, validating the effectiveness of ID2T in modeling domain-relevant cyberattack behaviors. In addition to strong classification performance, this work demonstrates how synthetic malicious traffic generation reduces the cost and complexity of cyberattack emulation. The proposed method offers a scalable and reproducible framework for training intrusion detection systems (IDS), highlighting the critical role of Artificial Intelligence (AI) in securing next-generation industrial vehicular networks.
{"title":"Synthetic Attack Dataset Generation With ID2T for AI-Based Intrusion Detection in Industrial V2I Network","authors":"Prinkle Sharma;Jaiganesh Anandan;Hong Liu;Jyoti Grover","doi":"10.1109/OJVT.2025.3609149","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3609149","url":null,"abstract":"Industrial Vehicle-to-Infrastructure (iV2I) networks are increasingly adopted in settings such as warehouses, construction sites, and smart factories to enhance automation and operational efficiency. However, these systems face growing cybersecurity risks that threaten safety-critical operations. This paper introduces a realistic synthetic dataset created using the ID2T framework, which injects malicious traffic, such as DDoS, PortScan, and memory corruption exploits, into benign communication traces collected from actual iV2I environments. The resulting hybrid dataset, combining synthetic and real-world traffic, enables the supervised training of a Multi-Layer Perceptron (MLP) neural network using 16 meticulously crafted flow-based features. Experimental results demonstrate high detection accuracy under both balanced and threat-specific conditions, validating the effectiveness of ID2T in modeling domain-relevant cyberattack behaviors. In addition to strong classification performance, this work demonstrates how synthetic malicious traffic generation reduces the cost and complexity of cyberattack emulation. The proposed method offers a scalable and reproducible framework for training intrusion detection systems (IDS), highlighting the critical role of Artificial Intelligence (AI) in securing next-generation industrial vehicular networks.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2509-2538"},"PeriodicalIF":4.8,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11159305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141681","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}
Pub Date : 2025-09-10DOI: 10.1109/OJVT.2025.3608287
Vineet Dhanawat;Varun Shinde;Rachid Alami;Adnan Akhunzada;Zaid Bin Faheem;Anjanava Biswas
The exponential increase in the adoption of Electric Vehicles (EVs) presents significant problems to the stability of the power grid. Therefore, it is crucial to accurately anticipate the demand for EV Charging Station (CS) to address this issue. To improve forecasts and identify CS load variables, existing studies are based on load profiling, which may be difficult to obtain for commercial EV charging stations. This paper proposes an efficient deep BiLSTMNet model to solve and mitigate these problems. Energy consumption and storage at four charging stations in California are analyzed. To guarantee accuracy and uniformity, the data is preprocessed by addressing missing values and ensuring consistency. A hybrid feature selection technique integrates the Boruta algorithm and SHAP (SHapley Additive exPlanations) values to ensure robust feature selection. The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model's hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). The model performs a time series analysis to identify daily, weekly, monthly, and seasonal patterns in EV charging demand. The integration of renewable energy sources—specifically solar and wind generation—into the EV charging infrastructure is thoroughly examined in this study, not merely as input features but as key factors influencing the stability of charging demand at various stations. Their temporal patterns and environmental dependencies are leveraged to enhance forecasting accuracy and ensure grid-aware demand management across charging stations. The proposed model's performance is evaluated using metrics such as R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Simulation results demonstrate the effectiveness of the proposed model, with an average R-squared value of 0.9, MAE of 2.15 kW, and RMSE of 2.75 kW across the four stations. The EfficientBiLSTMNet model shows superior predictive accuracy compared to traditional models, highlighting the importance of comprehensive feature selection and engineering in forecasting EV charging demand. This study provides a robust framework for predicting EV charging demand, integrating renewable energy sources to enhance the stability and sustainability of the power grid amidst the increasing penetration of EVs.
{"title":"Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet","authors":"Vineet Dhanawat;Varun Shinde;Rachid Alami;Adnan Akhunzada;Zaid Bin Faheem;Anjanava Biswas","doi":"10.1109/OJVT.2025.3608287","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3608287","url":null,"abstract":"The exponential increase in the adoption of Electric Vehicles (EVs) presents significant problems to the stability of the power grid. Therefore, it is crucial to accurately anticipate the demand for EV Charging Station (CS) to address this issue. To improve forecasts and identify CS load variables, existing studies are based on load profiling, which may be difficult to obtain for commercial EV charging stations. This paper proposes an efficient deep BiLSTMNet model to solve and mitigate these problems. Energy consumption and storage at four charging stations in California are analyzed. To guarantee accuracy and uniformity, the data is preprocessed by addressing missing values and ensuring consistency. A hybrid feature selection technique integrates the Boruta algorithm and SHAP (SHapley Additive exPlanations) values to ensure robust feature selection. The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model's hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). The model performs a time series analysis to identify daily, weekly, monthly, and seasonal patterns in EV charging demand. The integration of renewable energy sources—specifically solar and wind generation—into the EV charging infrastructure is thoroughly examined in this study, not merely as input features but as key factors influencing the stability of charging demand at various stations. Their temporal patterns and environmental dependencies are leveraged to enhance forecasting accuracy and ensure grid-aware demand management across charging stations. The proposed model's performance is evaluated using metrics such as R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Simulation results demonstrate the effectiveness of the proposed model, with an average R-squared value of 0.9, MAE of 2.15 kW, and RMSE of 2.75 kW across the four stations. The EfficientBiLSTMNet model shows superior predictive accuracy compared to traditional models, highlighting the importance of comprehensive feature selection and engineering in forecasting EV charging demand. This study provides a robust framework for predicting EV charging demand, integrating renewable energy sources to enhance the stability and sustainability of the power grid amidst the increasing penetration of EVs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2642-2661"},"PeriodicalIF":4.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11155205","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210152","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}
Pub Date : 2025-09-08DOI: 10.1109/OJVT.2025.3597609
José rodríguez-Piñeiro;Zhongxiang Wei;Jingjing Wang;Carlos A. Gutiérrez;Luis M. Correia
{"title":"Guest Editorial: Introduction to the Special Section on Current Research Trends and Open Challenges for 6G-Enabled Vehicle-to-Everything Networks","authors":"José rodríguez-Piñeiro;Zhongxiang Wei;Jingjing Wang;Carlos A. Gutiérrez;Luis M. Correia","doi":"10.1109/OJVT.2025.3597609","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3597609","url":null,"abstract":"","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2301-2304"},"PeriodicalIF":4.8,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011310","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}
Pub Date : 2025-09-05DOI: 10.1109/OJVT.2025.3606652
Saddam Hussain;Ali Tufail;Haji Awg Abdul Ghani Naim;Muhammad Asghar Khan;Gordana Barb
Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.
{"title":"A Lightweight Proxy Signature Scheme for Resource-Constrained NDN-Based Internet of Vehicles","authors":"Saddam Hussain;Ali Tufail;Haji Awg Abdul Ghani Naim;Muhammad Asghar Khan;Gordana Barb","doi":"10.1109/OJVT.2025.3606652","DOIUrl":"https://doi.org/10.1109/OJVT.2025.3606652","url":null,"abstract":"Named Data Networking (NDN) is considered a future architecture for content distribution in the Internet of Vehicles (IoV). The primary principles of NDN, which include naming and in-network caching, are perfectly aligned with the IoV requirements for time and location independence. Despite significant research efforts, full-scale deployment remains limited due to ongoing concerns regarding trust, safety, and security within the IoV network. Moreover, traditional security algorithms proposed for IoV are complex, with high computational demands that challenge the strict real-time constraints. To minimize the computational overhead of vehicles, we proposed an RSU-empowered proxy signature scheme for NDN-based IoV. The security of the proposed scheme is proven to be Existentially Unforgeable against Adaptive Chosen-Message Attacks (EU-ACMA) under the Random Oracle Model (ROM), considering the hardness of the Hyperelliptic Curve Discrete Logarithm Problem (HCDLP). A performance analysis, which considers both computation time and communication overhead, shows that the proposed scheme effectively minimizes these factors. Besides, we applied the Multi-Criteria Decision-Making (MCDM) technique, namely Evaluation based on Distance from Average Solution (EDAS), to meet the particular need to prioritize data in IoV. The findings show that the proposed scheme performs better than those in the related literature.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"6 ","pages":"2607-2626"},"PeriodicalIF":4.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210155","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：