UAV-Aided Wireless Energy Transfer for Sustaining Internet of Everything in 6G
Author:
Che Yueling1, Zhao Zeyu1, Luo Sheng1, Wu Kaishun1, Duan Lingjie2, Leung Victor C. M.1ORCID
Affiliation:
1. College of Computer and Software Engineering, Shenzhen University, Shenzhen 518060, China 2. Pillar of Engineering Systems and Design, Singapore University of Technology and Design, Singapore 487372, Singapore
Abstract
Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided.
Funder
National Natural Science Foundation of China Guangdong Natural Science Foundation Guangdong “Pearl River Talent Recruitment Program” Guangdong “Pearl River Talent Plan” Shenzhen Talents Special Project—Guangdong Provincial Innovation and Entrepreneurship Team Supporting Project Shenzhen Science and Technology Innovation Commission
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
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