Energy Efficiency Optimization of UAV-Assisted Wireless Powered Systems for Dependable Data Collections in Internet of Things

• Published in: IEEE transactions on reliability Vol. 72; no. 2; pp. 472 - 482
• Main Authors: Ma, Xiaohan, Na, Zhenyu, Lin, Bin, Liu, Lizhe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous aerial vehicles; Beamforming; Data collection; Data transmission; Downlink; Energy consumption; Energy efficiency; Internet of Things; Internet of Things (IoT); Iterative algorithms; Lagrange multiplier; Optimization; Propulsion; Resource management; Route planning; subtimeslot allocation; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Uplink; Wireless communication
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2022.3190371

Benefiting from high mobility, unmanned aerial vehicles (UAVs) can reconstruct wireless connections for affected areas. Most of the existing work has usually ignored the influence of limited airborne energy on the dependability of UAV data transmission. Accordingly, this article proposes an UAV-assisted wireless powered system to achieve dependable data collections in Internet of Things (IoT). Specifically, an UAV leverages energy beamforming to transfer energy to ground users (GUs) in downlink subtimeslot, while the GUs transmit data to the UAV with the harvested energy in uplink subtimeslot. For this system, a joint optimization problem of subtimeslot allocation and UAV route planning is investigated to maximize the system energy efficiency subject to UAV dynamics, time slot duration, and GUs' rate threshold. To tackle the nonconvexity of the formulated problem, a low-complexity alternating iterative algorithm is proposed. The first subproblem optimizes subtimeslot allocation by using the bisection method and Lagrange multiplier method for the fixed UAV route, while the second optimizes the UAV route for the periodic and single flight modes with the given subtimeslot allocation. Then, the two subproblems are alternatively solved until convergence. The simulation results demonstrate that the proposed algorithm can not only optimize the UAV route, but also achieve a good compromise between system throughput and UAV propulsion energy consumption.