A mobility compensation method for drones in SG-eIoT

Weng, Liguo; Zhang, Yanghui; Yang, Yong; Fang, Min; Yu, Zelong

doi:10.1016/j.dcan.2020.07.011

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Weng et al [ 70 ] suggested a method to improve mobility compensation of drone based IoT. The method involves phases of frequency offset approximation and relative velocity measurement.…”

Section: Recent Advancementsmentioning

confidence: 99%

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

Abdelmaboud

2021

Sensors

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The use of unmanned aerial vehicles or drones are a valuable technique in coping with issues related to life in the general public’s daily routines. Given the growing number of drones in low-altitude airspace, linking drones to form the Internet of drones (IoD) is a highly desirable trend to improve the safety as well as the quality of flight. However, there remain security, privacy, and communication issues related to IoD. In this paper, we discuss the key requirements of security, privacy, and communication and we present a taxonomy of IoD based on the most relevant considerations. Furthermore, we present the most commonly used commercial case studies and address the latest advancements and solutions proposed for the IoD environments. Lastly, we discuss the challenges and future research directions of IoD.

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“…Weng et al [ 70 ] suggested a method to improve mobility compensation of drone based IoT. The method involves phases of frequency offset approximation and relative velocity measurement.…”

Section: Recent Advancementsmentioning

confidence: 99%

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

Abdelmaboud

2021

Sensors

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“…With the rapid advancement of technology, unmanned aerial vehicles (UAVs) have found numerous urban IoT (Internet of Things) applications in fields such as rescue operations [1], surveillance [2,3], edge computing [4,5], disaster area reconstruction [6,7], aerial base stations [8,9], intelligent transportation [10,11], wireless power transfer [12], environmental monitoring [13], and more [14,15]. According to market forecasts, the UAV market will experience a compound annual growth rate (CAGR) of 7.9%, expanding from USD 26.2 billion in 2022 to USD 38.3 billion in 2027 [16].…”

Section: Introductionmentioning

confidence: 99%

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Yan,

Fu,

Lin

et al. 2023

Applied Sciences

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Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security.

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“…With the exponential growth of data-driven applications and the increasing demand for high-speed and ubiquitous communication services, the integration of edge-cloud computing [1,2] and aerial wireless networks has become a cornerstone of modern communication systems [3,4]. The aerial base station (AeBS) is a mobile base station that installs communication equipment [5] on the aerial platform, such as the unmanned aerial vehicle (UAV) [6,7]. AeBSs can offload computationally intensive tasks to cloud servers, enabling efficient data processing, storage, and analysis.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph Convolution Mix DDPG for Multi-Aerial Base Station Deployment

He,

Zhou,

Zhao

et al. 2023

Preprint

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Aerial base station (AeBS), as crucial components of air-ground integrated networks, can serve as the edge nodes of the edge-cloud computing network. Optimizing the deployment of multiple AeBSs to maximize system energy efficiency is currently a prominent and actively researched topic. In this paper, we deploy AeBSs using multi-agent deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized partially observable Markov decision process (Dec-POMDP), taking into consideration the constrained observation range of AeBSs. The hypergraph convolution mix deep deterministic policy gradient (HCMIX-DDPG) algorithm is designed to maximize the system energy efficiency. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem, and hypergraph convolutional (HGCN) network is introduced to strengthen the cooperative relationship between agents. The suggested HCMIX-DDPG algorithm outperforms alternative baseline algorithms in the multi-AeBS deployment scenario, according to simulation results.

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A mobility compensation method for drones in SG-eIoT

Cited by 9 publications

References 22 publications

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends

UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey

Hypergraph Convolution Mix DDPG for Multi-Aerial Base Station Deployment

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