Integrated Sensing and Communications for UAV Communications With Jittering Effect

Zhao, Jianwei; Gao, Feifei; Jia, Weimin; Yuan, Wanmai; Jin, Wei

doi:10.1109/lwc.2023.3243590

Cited by 53 publications

(19 citation statements)

References 11 publications

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“…This implies the importance of beam alignment accuracy for UAV communication. The recent works on improving the accuracy of beam alignment for UAV communication can refer to the literature, e.g., [48], [49]. Moreover, we can see that MAPAS performs better than CDAS in terms of the outage probability, since the closest UAV may not be able to provide the strongest signal, especially under the probabilistic channel model.…”

Section: A Analysis Validationmentioning

confidence: 80%

The School of History and the Institute for Global History, Beijing Foreign Studies University

Wang¹

2021

Journal of Cultural Interaction in East Asia

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The School of History and the Institute for Global History of Beijing Foreign Studies University was founded in December 2017. It aims to become a significant platform in China for international scholars to conduct research on global history. The school offers course on the histories of China and regions across the world from a global perspective. In terms of research, it is the school’s goal to understand history through interactions, especially the interaction between China and the world in modern terms. The school pays special attention to the fields of the history of ideas, the knowledge transfer and scholarly exchange, and the cultural and linguistic contact between the East and the West.

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Section: A Analysis Validationmentioning

confidence: 80%

The School of History and the Institute for Global History, Beijing Foreign Studies University

Wang¹

2021

Journal of Cultural Interaction in East Asia

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“…Authors in [ 43 – 45 ] propose a scheduler algorithm for multi-path propagation using Multi-path Transmission Control Protocol (MPTCP), reducing the network’s energy consumption. By offline processing the Markov decision, they analyse multiple application schedules, depending on the earlier history of communications and various radio model energy interfaces.…”

Section: Related Workmentioning

confidence: 99%

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Aalsalem

2024

PLoS ONE

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Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network’s status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs’ capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

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“…Liu et al [131] used Markov decision to model the vehicle trajectory, which improves the accuracy of trajectory matching. Zhao et al [132] presented a channel estimation method which uses sensing, communication and control technologies to obtain the information needed for trajectory generation.…”

Section: Deep Learning Based Multi-object Multi-camera Trackingmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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Integrated Sensing and Communications for UAV Communications With Jittering Effect

Cited by 53 publications

References 11 publications

The School of History and the Institute for Global History, Beijing Foreign Studies University

The School of History and the Institute for Global History, Beijing Foreign Studies University

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

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