This paper presents a novel method for using full duplex (FD) communication across shared channels in cellular vehicle-to-everything (C-V2X) networks. Three modes A, B and C have been defined for communication in V2X networks where Mode C for FD communication has been introduced for the first time. We have derived mathematical model for success probability of FD for C-V2X network and formulated expressions for area spectral efficiency (ASE). Dual connectivity (DC) for simultaneous link of receiving vehicle with nearest transmitting vehicle and base station (BS) has also been analyzed for the first time for HD and FD in C-V2X network. Analytical and Monte Carlo simulations results have shown that utilization of FD in C-V2X network provides comparable success probability as compared to HD with improvement in ASE. Success probability of FD remains close to HD in terms of signal to interference noise ratio (SINR) in the range from -40 dBW to 60 dBW. Importance of achieving perfect self-interference cancellation (SIC) for different values of self-interference (SI) in FD network has also been evaluated. FD in C-V2X network has shown to significantly improve ASE with gain of 2.55 dB over Direct Short Range Communication (DSRC) and 2 dB over HD in C-V2X network under specific conditions. No degradation in ASE was observed in case of DC for HD and FD. ASE for FD has shown improvement as compared to HD for DSRC and C-V2X networks when evaluated against density of vehicles, BSs and roads.
本文介绍了一种在蜂窝式车对物（C-V2X）网络的共享信道上使用全双工（FD）通信的新方法。V2X 网络中的通信定义了三种模式 A、B 和 C，其中首次引入了用于 FD 通信的模式 C。我们推导出了 C-V2X 网络 FD 成功概率的数学模型，并制定了区域频谱效率 (ASE) 的表达式。我们还首次分析了 C-V2X 网络中高清和远距离传输的双连接（DC），即接收车与最近的发射车和基站（BS）同时链接。分析和蒙特卡罗模拟结果表明，在 C-V2X 网络中使用 FD 与 HD 相比，成功概率相当，但 ASE 有所提高。在 -40 dBW 至 60 dBW 范围内，就信号干扰噪声比 (SINR) 而言，FD 的成功概率与 HD 接近。此外，还评估了在 FD 网络中针对不同的自干扰（SI）值实现完美自干扰消除（SIC）的重要性。在特定条件下，C-V2X 网络中的 FD 可显著改善 ASE，与直接短程通信 (DSRC) 相比增益为 2.55 dB，与 C-V2X 网络中的 HD 相比增益为 2 dB。在直流情况下，HD 和 FD 的 ASE 没有下降。在根据车辆、BS 和道路密度进行评估时，在 DSRC 和 C-V2X 网络中，与 HD 相比，FD 的 ASE 有所提高。
{"title":"Coverage Probability and Area Spectral Efficiency of Full Duplex Communication in C-V2X Network With Dual Connectivity","authors":"Adeel Ahmad;Muhammad Nadeem Sial;Junaid Ahmed;Sadiq Ullah","doi":"10.1109/OJVT.2024.3443665","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3443665","url":null,"abstract":"This paper presents a novel method for using full duplex (FD) communication across shared channels in cellular vehicle-to-everything (C-V2X) networks. Three modes A, B and C have been defined for communication in V2X networks where Mode C for FD communication has been introduced for the first time. We have derived mathematical model for success probability of FD for C-V2X network and formulated expressions for area spectral efficiency (ASE). Dual connectivity (DC) for simultaneous link of receiving vehicle with nearest transmitting vehicle and base station (BS) has also been analyzed for the first time for HD and FD in C-V2X network. Analytical and Monte Carlo simulations results have shown that utilization of FD in C-V2X network provides comparable success probability as compared to HD with improvement in ASE. Success probability of FD remains close to HD in terms of signal to interference noise ratio (SINR) in the range from -40 dBW to 60 dBW. Importance of achieving perfect self-interference cancellation (SIC) for different values of self-interference (SI) in FD network has also been evaluated. FD in C-V2X network has shown to significantly improve ASE with gain of 2.55 dB over Direct Short Range Communication (DSRC) and 2 dB over HD in C-V2X network under specific conditions. No degradation in ASE was observed in case of DC for HD and FD. ASE for FD has shown improvement as compared to HD for DSRC and C-V2X networks when evaluated against density of vehicles, BSs and roads.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1256-1272"},"PeriodicalIF":5.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142275015","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 : 2024-08-14DOI: 10.1109/OJVT.2024.3443675
Ryan Wen Liu;Shiqi Zhou;Shangkun Yin;Yaqing Shu;Maohan Liang
With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AIS data lead to high storage requirements and computing costs, as well as low data transmission efficiency. These challenges highlight the critical importance of vessel trajectory compression for surface vehicles. However, the complexity and diversity of vessel trajectories and behaviors make trajectory compression imperative and challenging in maritime applications. Therefore, trajectory compression has been one of the hot spots in research on trajectory data mining. The major purpose of this work is to provide a comprehensive reference source for beginners involved in vessel trajectory compression. The current trajectory compression methods could be broadly divided into two types, batch (offline) and online modes. The principles and pseudo-codes of these methods will be provided and discussed in detail. In addition, compressive experiments on several publicly available data sets have been implemented to evaluate the batch and online compression methods in terms of computation time, compression ratio, trajectory similarity, and trajectory length loss rate. Finally, we develop a flexible and open software, called AISCompress , for AIS-based batch and online vessel trajectory compression. The conclusions and associated future works are also given to inspire future applications in vessel trajectory compression.
{"title":"AIS-Based Vessel Trajectory Compression: A Systematic Review and Software Development","authors":"Ryan Wen Liu;Shiqi Zhou;Shangkun Yin;Yaqing Shu;Maohan Liang","doi":"10.1109/OJVT.2024.3443675","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3443675","url":null,"abstract":"With the advancement of satellite and 5G communication technologies, vehicles can transmit and exchange data from anywhere in the world. It has resulted in the generation of massive spatial trajectories, particularly from the Automatic Identification System (AIS) for surface vehicles. The massive AIS data lead to high storage requirements and computing costs, as well as low data transmission efficiency. These challenges highlight the critical importance of vessel trajectory compression for surface vehicles. However, the complexity and diversity of vessel trajectories and behaviors make trajectory compression imperative and challenging in maritime applications. Therefore, trajectory compression has been one of the hot spots in research on trajectory data mining. The major purpose of this work is to provide a comprehensive reference source for beginners involved in vessel trajectory compression. The current trajectory compression methods could be broadly divided into two types, batch (offline) and online modes. The principles and pseudo-codes of these methods will be provided and discussed in detail. In addition, compressive experiments on several publicly available data sets have been implemented to evaluate the batch and online compression methods in terms of computation time, compression ratio, trajectory similarity, and trajectory length loss rate. Finally, we develop a flexible and open software, called \u0000<italic>AISCompress</i>\u0000, for AIS-based batch and online vessel trajectory compression. The conclusions and associated future works are also given to inspire future applications in vessel trajectory compression.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1193-1214"},"PeriodicalIF":5.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233073","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}
This study realizes firmware-agnostic line-of-sight (LOS) identification to extend the range of WiFi-sensing applications. We developed a beamforming feedback (BFF)-based LOS identification algorithm. BFF frames are transmitted for multiple-input multiple-output (MIMO) communications. They can be obtained by capturing frames without custom firmware or specific chipsets and contain a beamforming feedback matrix (BFM) and subcarrier-averaged stream gain (SSG). These provide partial channel state information (CSI), and there are two major calculation steps involved from the CSI to the BFF: unquantized BFF (UQBFF) calculation and quantization. Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. Therefore, the proposed BFF-based method extracts features from the first-column vectors of the BFM. In addition, SSGs were leveraged to improve the accuracy. To demonstrate the feasibility of the proposed method, we conducted experiments using commodity off-the-shelf devices compliant with the IEEE 802.11ac standard. In the experimental evaluation, the proposed BFF-based method achieved an identification accuracy of 75.0%, whereas the CSI-based method achieved an accuracy of 81.2%. Accuracy comparisons revealed that the accuracy degradation of the BFF-based identification from the CSI-based identification was primarily caused by UQBFF calculations rather than quantization.
{"title":"Beamforming Feedback-Based Line-of-Sight Identification Toward Firmware-Agnostic WiFi Sensing","authors":"Hiroki Shimomura;Koji Yamamoto;Takayuki Nishio;Akihito Taya","doi":"10.1109/OJVT.2024.3440400","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3440400","url":null,"abstract":"This study realizes firmware-agnostic line-of-sight (LOS) identification to extend the range of WiFi-sensing applications. We developed a beamforming feedback (BFF)-based LOS identification algorithm. BFF frames are transmitted for multiple-input multiple-output (MIMO) communications. They can be obtained by capturing frames without custom firmware or specific chipsets and contain a beamforming feedback matrix (BFM) and subcarrier-averaged stream gain (SSG). These provide partial channel state information (CSI), and there are two major calculation steps involved from the CSI to the BFF: unquantized BFF (UQBFF) calculation and quantization. Focusing on the relationship between singular value decomposition and principal component analysis, we numerically demonstrated that the first column vectors of the BFM reflect the LOS/NLOS conditions. Therefore, the proposed BFF-based method extracts features from the first-column vectors of the BFM. In addition, SSGs were leveraged to improve the accuracy. To demonstrate the feasibility of the proposed method, we conducted experiments using commodity off-the-shelf devices compliant with the IEEE 802.11ac standard. In the experimental evaluation, the proposed BFF-based method achieved an identification accuracy of 75.0%, whereas the CSI-based method achieved an accuracy of 81.2%. Accuracy comparisons revealed that the accuracy degradation of the BFF-based identification from the CSI-based identification was primarily caused by UQBFF calculations rather than quantization.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1024-1035"},"PeriodicalIF":5.3,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10631655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013198","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 : 2024-08-02DOI: 10.1109/OJVT.2024.3437470
Praveen Sai Bere;Mohammed Zafar Ali Khan;Lajos Hanzo
V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a $mathscr {C}(n,k)$ code having a minimum distance ${d_{min }}$ , the proposed decoder incurs a worst-case complexity order of $2^{({d_{min }}-1)}-1$ . Notably, for codes having low ${d_{min }}$ , this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder.
{"title":"A Low-Complexity Diversity-Preserving Universal Bit-Flipping Enhanced Hard Decision Decoder for Arbitrary Linear Codes","authors":"Praveen Sai Bere;Mohammed Zafar Ali Khan;Lajos Hanzo","doi":"10.1109/OJVT.2024.3437470","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3437470","url":null,"abstract":"V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains diversity order, albeit at the cost of increased computational complexity. We introduce a novel enhanced hard-decision decoder termed as the Diversity Flip decoder (DFD) designed for preserving the diversity order. Moreover, it exhibits ‘universal’ applicability to all linear block codes. For a \u0000<inline-formula><tex-math>$mathscr {C}(n,k)$</tex-math></inline-formula>\u0000 code having a minimum distance \u0000<inline-formula><tex-math>${d_{min }}$</tex-math></inline-formula>\u0000, the proposed decoder incurs a worst-case complexity order of \u0000<inline-formula><tex-math>$2^{({d_{min }}-1)}-1$</tex-math></inline-formula>\u0000. Notably, for codes having low \u0000<inline-formula><tex-math>${d_{min }}$</tex-math></inline-formula>\u0000, this complexity represents a significant reduction compared to the popular soft and hard decision decoding algorithms. Due to its capability of maintaining diversity at a low complexity, it is eminently suitable for applications such as V2X (Vehicle-to-everything), IoT (Internet of Things), mMTC (Massive Machine type Communications), URLLC (Ultra-Reliable Low Latency Communications) and WBAN (Wireless Body Area Networks) for efficient decoding with favorable performance characteristics. The simulation results provided for various known codes and decoding algorithms validate the performance versus complexity benefits of the proposed decoder.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1496-1517"},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10621450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452772","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 : 2024-08-02DOI: 10.1109/OJVT.2024.3436857
Hang Mi;Bo Ai;Ruisi He;Anuraag Bodi;Raied Caromi;Jian Wang;Jelena Senic;Camillo Gentile;Yang Miao
Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician $K$ factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.
{"title":"Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud","authors":"Hang Mi;Bo Ai;Ruisi He;Anuraag Bodi;Raied Caromi;Jian Wang;Jelena Senic;Camillo Gentile;Yang Miao","doi":"10.1109/OJVT.2024.3436857","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3436857","url":null,"abstract":"Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. It is difficult to characterize the time-varying characteristics of mmWave channels through statistical models, e.g. slope-intercept models for path loss and lognormal models for delay spread and angular spread. Therefore, highly accurate channel modeling and prediction are necessary for deployment of mmWave communication systems. In this paper, a mmWave channel parameter prediction method using deep learning and environment point cloud is proposed. The parameters predicted include path loss, root-mean-square (RMS) delay spread, angular spread and Rician \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 factor. First, we introduce a novel measurement campaign for indoor mmWave channel at 60 GHz, where a light detection and ranging (LiDAR) sensor and panoramic camera were co-located with a channel sounder and then time-synchronized point clouds and images were captured to describe environmental information. Furthermore, a fusion method between the point clouds and images is proposed based on geometric relationship between the LiDAR and camera, to compress the size of the data collected. Second, based on a classic point cloud classification model (PointNet), we propose a novel regression PointNet model applied to channel parameter prediction. To overcome generalization problem of model under limited measurements, an area-by-area training and testing method is proposed. Third, we evaluate the proposed prediction model and training method, by comparing prediction results with measured ground truth. To provide insights on what training inputs are best, we demonstrate the impacts of different combinations of input information on prediction accuracy. Last, the deployment and implementation method of the proposed model is recommended to the readers.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1059-1072"},"PeriodicalIF":5.3,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142077608","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 : 2024-07-31DOI: 10.1109/OJVT.2024.3436065
Adel Gohari;Anuar Bin Ahmad;Lawali Rabiu;Ruzairi Bin Abdul Rahim;A.S.M. Supa'at;Nassrin Ibrahim Mohamed Elamin;Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Rozeha A. Rashid;Sophan Wahyudi Nawawi;Nazri Nasir;Mohd Adib Bin Sarijari;Norhadija B. Darwin;Ali H. Muqaibel
Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, we also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.
{"title":"A Systematic Review of the UAV Technology Usage in ASEAN","authors":"Adel Gohari;Anuar Bin Ahmad;Lawali Rabiu;Ruzairi Bin Abdul Rahim;A.S.M. Supa'at;Nassrin Ibrahim Mohamed Elamin;Mohammed Salih Mohammed Gismalla;Suhail I. Al-Dharrab;Rozeha A. Rashid;Sophan Wahyudi Nawawi;Nazri Nasir;Mohd Adib Bin Sarijari;Norhadija B. Darwin;Ali H. Muqaibel","doi":"10.1109/OJVT.2024.3436065","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3436065","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, we also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1036-1058"},"PeriodicalIF":5.3,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10616173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142045114","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 : 2024-07-30DOI: 10.1109/OJVT.2024.3435489
Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso
Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.
{"title":"Comparison and Analysis of Algorithms for Coordinated EV Charging to Reduce Power Grid Impact","authors":"Cesar Diaz-Londono;Paolo Maffezzoni;Luca Daniel;Giambattista Gruosso","doi":"10.1109/OJVT.2024.3435489","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3435489","url":null,"abstract":"Electric vehicle (EV) adoption has been increasing rapidly, posing new challenges for integrating EV charging infrastructure with the existing electrical grid. Uncoordinated charging of EVs can cause transformers to overload, leading to instability and unreliability in the grid. This article introduces two smart charging coordinators for EV charging pools designed to manage EV charging while considering transformer power limits. The first strategy aims to minimize operational costs, while the second maximizes the charger flexibility. Both coordinators account for uncertainties in EV arrival time and state of charge, as well as inflexible demands on transformers. The strategies are evaluated and compared using grid-aware and grid-unaware methods regarding transformer power limits. Real-world datasets are utilized to assess the performance of the proposed strategies through simulation studies across three scenarios: single charging station behavior, average parking lot occupancy, and worst-case occupancy scenarios. Comparative analysis against uncoordinated and coordinated strategies from the literature reveals that the flexibility maximization strategy provides the most uniform response, effectively mitigating transformer overload events by optimizing charging power and scheduling flexibility. The study underscores the importance of accurate, innovative charging strategies for seamless EV integration and emphasizes the necessity of coordinated charging pools for reliable EV charging operations.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"990-1003"},"PeriodicalIF":5.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965057","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 : 2024-07-26DOI: 10.1109/OJVT.2024.3434486
Atefeh Hajijamali Arani;Peng Hu;Yeying Zhu
Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.
{"title":"UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms","authors":"Atefeh Hajijamali Arani;Peng Hu;Yeying Zhu","doi":"10.1109/OJVT.2024.3434486","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3434486","url":null,"abstract":"Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. We consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Our simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, we aim to provide design and deployment guidelines for UAV-assisted SAGINs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1004-1023"},"PeriodicalIF":5.3,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10612249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998659","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 : 2024-07-19DOI: 10.1109/OJVT.2024.3431449
Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela
In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.
{"title":"Enhancing Wheel Vertical Displacement Estimation in Road Vehicles Through Integration of Model-Based Estimator With Artificial Intelligence","authors":"Raffaele Marotta;Sebastiaan van Aalst;Kylian Praet;Miguel Dhaens;Valentin Ivanov;Salvatore Strano;Mario Terzo;Ciro Tordela","doi":"10.1109/OJVT.2024.3431449","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3431449","url":null,"abstract":"In the automotive industry, the accurate estimation of wheel displacements is crucial for optimizing vehicle suspension systems. Traditional model-based approaches often face challenges in accurately predicting these displacements due to the complex dynamics of the road-vehicle interaction. To address this limitation, this study, conducted in the frame of the OWHEEL project, proposes the integration of a multi-output neural network capable of compensating for estimation errors inherent in model-based approaches, specifically those arising from road inputs. Leveraging only vertical acceleration measurements, the neural network operates in parallel with the model-based estimator, enhancing the overall accuracy of displacement estimation. Experimental validation using a sports vehicle demonstrates the efficacy of the proposed methodology, showcasing its ability to improve estimation accuracy beyond the capabilities of the model-based approach alone.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"979-989"},"PeriodicalIF":5.3,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965056","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 : 2024-07-18DOI: 10.1109/OJVT.2024.3430818
Mohamed Ben Bezziane;Siham Hasan;Bouziane Brik;Fathi Eltayeeb Abukhres;Ali Algaddafi;Amina Ben Bezziane;Ahmed Korichi;Mohamed Redouane Kafi
The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.
{"title":"Game Theory-Based UAV-Cloud for Service Selection Architecture in Flying Ad Hoc Networks","authors":"Mohamed Ben Bezziane;Siham Hasan;Bouziane Brik;Fathi Eltayeeb Abukhres;Ali Algaddafi;Amina Ben Bezziane;Ahmed Korichi;Mohamed Redouane Kafi","doi":"10.1109/OJVT.2024.3430818","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3430818","url":null,"abstract":"The rapid progression of Cloud Computing (CC) technology has ushered in innovative ecosystem concepts such as Mobile Cloud Computing (MCC). In this context, the incorporation of Unmanned Aerial Vehicles (UAVs) into these cloud ecosystems has unlocked new avenues for use cases such as delivery services, disaster response, and surveillance. However, this integration presents challenges in resource management and service selection due to the unique constraints of drones and variations in service quality. This paper proposes a Game Theory-based UAV-cloud of Service Selection Architecture (GT-SSA) to address resource management and service selection challenges. By leveraging game theory in our proposal, GT-SSA optimizes decision-making for Client Drones and Provider Drones, enhancing service selection efficiency. GT-SSA proved its resilience to scalability concerns, as evidenced in Discovery Delay, Consumption Delay, End-to-End Delay, and Energy consumption. Moreover, when GT-SSA is compared with the Game Theory approach for Cloud Services in MEC- and UAV-enabled networks (GTCS), GT-SSA outperforms GTCS in terms of Successful Execution Rate, Average Execution Time, and Energy consumption. Our research also reveals that game theory surpasses fuzzy logic in terms of service selection efficiency.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1692-1711"},"PeriodicalIF":5.3,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10602763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636473","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}
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