Pub Date : 2024-08-21DOI: 10.1109/OJVT.2024.3446799
Shumaila Javaid;Hamza Fahim;Bin He;Nasir Saeed
Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.
{"title":"Large Language Models for UAVs: Current State and Pathways to the Future","authors":"Shumaila Javaid;Hamza Fahim;Bin He;Nasir Saeed","doi":"10.1109/OJVT.2024.3446799","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3446799","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1166-1192"},"PeriodicalIF":5.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169712","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}
Future drone-enabled Internet-of-Things (IoT) wireless networks have attracted considerable attention from industry and academia. Future drone-enabled IoT wireless networks are expected to enable the Internet of Everything and provide services with massive connectivity, heterogeneous quality of service, ultra-reliability, and higher throughput. Therefore, future drone-enabled IoT wireless networks necessitate more effective use of wireless resources and efficient interference management approaches. As a result, the multiple access techniques and the physical layer for wireless communication systems have been rethought and redesigned. This paper proposes utilizing the partial non-orthogonal multiple access (P-NOMA) in drone-enabled IoT wireless networks, where a single drone provides wireless coverage for a set of IoT devices. In P-NOMA, a portion of the channel is orthogonal, while the other is non-orthogonal for each IoT device. When using a non-orthogonal channel portion, an IoT device that receives high transmit power from the drone treats a signal of another IoT device as noise and quickly recovers its signal without using a successive interference cancellation (SIC) process. However, an IoT device that receives low transmit power from that drone must perform the SIC process on a non-orthogonal channel portion to recover its signal. The optimization problem in this research aims to find the maximum sum data rate of all IoT devices, considering the 3D placement of the drone, device pairing, and the parameters of P-NOMA. Finding the optimal solution to the optimization problem is challenging because of the NP-completeness of the formulated problem. Therefore, a decomposition framework is proposed to aid in solving it. Particularly, the optimization problem is decomposed into three subproblems: the 3D placement for the drone, device pairing, and P-NOMA parameters. Then efficient techniques are proposed to solve these subproblems. Simulation results verify the efficacy of utilizing P-NOMA in drone-enabled IoT wireless networks. Specifically, our results demonstrate that P-NOMA can boost the sum rate by 22%–28% compared with NOMA and by 83%–104% compared with OMA.
未来的无人机物联网（IoT）无线网络已引起业界和学术界的广泛关注。未来的无人机物联网无线网络有望实现万物互联，并提供具有大规模连接、异构服务质量、超高可靠性和更高吞吐量的服务。因此，未来的无人机物联网无线网络需要更有效地利用无线资源和高效的干扰管理方法。因此，人们对无线通信系统的多址接入技术和物理层进行了重新思考和设计。本文提出在无人机支持的物联网无线网络中使用部分非正交多址接入（P-NOMA），即由一架无人机为一组物联网设备提供无线覆盖。在 P-NOMA 中，每个物联网设备的部分信道是正交的，而另一部分是非正交的。在使用非正交信道部分时，从无人机接收到高发射功率的物联网设备会将另一个物联网设备的信号视为噪声，并在不使用连续干扰消除（SIC）过程的情况下快速恢复其信号。然而，从该无人机接收低发射功率的物联网设备必须在非正交信道部分执行 SIC 过程才能恢复其信号。考虑到无人机的三维位置、设备配对和 P-NOMA 的参数，本研究的优化问题旨在找到所有物联网设备的最大数据速率总和。由于所提问题的 NP 完备性，寻找优化问题的最优解具有挑战性。因此，我们提出了一个分解框架来帮助解决问题。特别是，优化问题被分解成三个子问题：无人机的 3D 放置、设备配对和 P-NOMA 参数。然后提出了解决这些子问题的高效技术。仿真结果验证了在无人机支持的物联网无线网络中使用 P-NOMA 的有效性。具体而言，我们的结果表明，与 NOMA 相比，P-NOMA 可将总和率提高 22%-28%；与 OMA 相比，P-NOMA 可将总和率提高 83%-104%。
{"title":"Utilizing Partial Non-Orthogonal Multiple Access (P-NOMA) in Drone-Enabled Internet-of-Things Wireless Networks","authors":"Hazim Shakhatreh;Sharief Abdel-Razeq;Ala Al-Fuqaha","doi":"10.1109/OJVT.2024.3445768","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3445768","url":null,"abstract":"Future drone-enabled Internet-of-Things (IoT) wireless networks have attracted considerable attention from industry and academia. Future drone-enabled IoT wireless networks are expected to enable the Internet of Everything and provide services with massive connectivity, heterogeneous quality of service, ultra-reliability, and higher throughput. Therefore, future drone-enabled IoT wireless networks necessitate more effective use of wireless resources and efficient interference management approaches. As a result, the multiple access techniques and the physical layer for wireless communication systems have been rethought and redesigned. This paper proposes utilizing the partial non-orthogonal multiple access (P-NOMA) in drone-enabled IoT wireless networks, where a single drone provides wireless coverage for a set of IoT devices. In P-NOMA, a portion of the channel is orthogonal, while the other is non-orthogonal for each IoT device. When using a non-orthogonal channel portion, an IoT device that receives high transmit power from the drone treats a signal of another IoT device as noise and quickly recovers its signal without using a successive interference cancellation (SIC) process. However, an IoT device that receives low transmit power from that drone must perform the SIC process on a non-orthogonal channel portion to recover its signal. The optimization problem in this research aims to find the maximum sum data rate of all IoT devices, considering the 3D placement of the drone, device pairing, and the parameters of P-NOMA. Finding the optimal solution to the optimization problem is challenging because of the NP-completeness of the formulated problem. Therefore, a decomposition framework is proposed to aid in solving it. Particularly, the optimization problem is decomposed into three subproblems: the 3D placement for the drone, device pairing, and P-NOMA parameters. Then efficient techniques are proposed to solve these subproblems. Simulation results verify the efficacy of utilizing P-NOMA in drone-enabled IoT wireless networks. Specifically, our results demonstrate that P-NOMA can boost the sum rate by 22%–28% compared with NOMA and by 83%–104% compared with OMA.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1088-1105"},"PeriodicalIF":5.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640187","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090964","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 : 2024-08-16DOI: 10.1109/OJVT.2024.3443630
Joel Andrew Miller;Soodeh Nikan;Mohamed H. Zaki
Autonomous vehicles (AVs) represent a transformative advance in automotive technology, promising increased safety and efficiency by reducing human error. However, integrating human factors remains a critical challenge, especially during takeover scenarios where the human driver must re-assume control of the vehicle. This review paper focuses on the engineering and human-centred design of takeover requests (TORs) within Level 3 autonomous vehicles, emphasizing the importance of seamless transitions between automated driving and manual control. We explore the concept of the Operational Design Domain (ODD), which dictates the specific conditions under which an AV may safely operate, and contextualize its role. Through a comprehensive analysis, we highlight how monitoring both the internal and external environment, and improving human-machine interfaces through the design of takeover requests (TOR), play pivotal roles in ensuring that transitions are safe and efficient. We argue for the necessity of integrating detailed human factors and ergonomic considerations to foster a human-centred approach in AV design. We aim to establish a symbiotic relationship between human drivers and autonomous systems, ensuring that AVs not only function optimally within their designated ODD, but also maintain high safety standards during critical takeover moments.
{"title":"Navigating the Handover: Reviewing Takeover Requests in Level 3 Autonomous Vehicles","authors":"Joel Andrew Miller;Soodeh Nikan;Mohamed H. Zaki","doi":"10.1109/OJVT.2024.3443630","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3443630","url":null,"abstract":"Autonomous vehicles (AVs) represent a transformative advance in automotive technology, promising increased safety and efficiency by reducing human error. However, integrating human factors remains a critical challenge, especially during takeover scenarios where the human driver must re-assume control of the vehicle. This review paper focuses on the \u0000<italic>engineering and human-centred design of takeover requests (TORs) within Level 3 autonomous vehicles</i>\u0000, emphasizing the importance of seamless transitions between automated driving and manual control. We explore the concept of the Operational Design Domain (ODD), which dictates the specific conditions under which an AV may safely operate, and contextualize its role. Through a comprehensive analysis, we highlight how monitoring both the internal and external environment, and improving human-machine interfaces through the design of takeover requests (TOR), play pivotal roles in ensuring that transitions are safe and efficient. We argue for the necessity of integrating detailed human factors and ergonomic considerations to foster a human-centred approach in AV design. We aim to establish a symbiotic relationship between human drivers and autonomous systems, ensuring that AVs not only function optimally within their designated ODD, but also maintain high safety standards during critical takeover moments.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1073-1087"},"PeriodicalIF":5.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142090965","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}
Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in real-life field tests. Results have indicated that the E2E network latency of the perception information sent is around 30ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.
{"title":"Enhancing Reliability in Infrastructure-Based Collective Perception: A Dual-Channel Hybrid Delivery Approach With Real-Time Monitoring","authors":"Yu Asabe;Ehsan Javanmardi;Jin Nakazato;Manabu Tsukada;Hiroshi Esaki","doi":"10.1109/OJVT.2024.3443877","DOIUrl":"https://doi.org/10.1109/OJVT.2024.3443877","url":null,"abstract":"Standalone autonomous vehicles primarily rely on their onboard sensors and may have blind spots or limited situational awareness in complex or dynamic traffic scenarios, leading to difficulties in making safe decisions. Collective perception enables connected autonomous vehicles (CAVs) to overcome the limitations of standalone autonomous vehicles by sharing sensory information with nearby road users. However, unfavorable conditions of the wireless communication medium it uses can lead to limited reliability and reduced quality of service. In this paper, we propose methods for increasing the reliability of collective perception through real-time packet delivery rate monitoring and a dual-channel hybrid delivery approach. We have implemented AutowareV2X, a vehicle-to-everything (V2X) communication module integrated into the autonomous driving (AD) software Autoware. AutowareV2X provides connectivity to the AD stack, enabling end-to-end (E2E) experimentation and evaluation of CAVs. The Collective Perception Service (CPS) was also implemented, allowing the transmission of Collective Perception Messages (CPMs). Our proposed methods using AutowareV2X were evaluated using actual hardware and vehicles in real-life field tests. Results have indicated that the E2E network latency of the perception information sent is around 30ms, and the AD software can use shared object data to conduct collision avoidance maneuvers. The dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information. This enabled the reliable transmission of CPMs even when there was significant packet loss on one of the transmitting channels.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1124-1138"},"PeriodicalIF":5.3,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142152101","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 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}
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：