Wireless-Powered Mobile Crowdsensing Enhanced by UAV-Mounted RIS: Joint Transmission, Compression, and Trajectory Design

TL;DR

This paper proposes a novel UAV-mounted RIS framework to enhance wireless power transfer and data collection in mobile crowdsensing, optimizing transmission, compression, and trajectory to improve system efficiency.

Contribution

It introduces a joint UAV-RIS deployment and a comprehensive optimization framework for WPT-assisted MCS, including novel algorithms for trajectory and beamforming design.

Findings

Data collection is significantly increased with the proposed framework.

Compression schemes' energy efficiency gains diminish with higher power usage.

Simulation confirms improved data throughput in wireless-powered MCS systems.

Abstract

Mobile crowdsensing (MCS) enables data collection from massive devices to achieve a wide sensing range. Wireless power transfer (WPT) is a promising paradigm for prolonging the operation time of MCS systems by sustainably transferring power to distributed devices. However, the efficiency of WPT significantly deteriorates when the channel conditions are poor. Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) can serve as active or passive relays to enhance the efficiency of WPT in unfavourable propagation environments. Therefore, to explore the potential of jointly deploying UAVs and RISs to enhance transmission efficiency, we propose a novel transmission framework for the WPT-assisted MCS systems, which is enhanced by a UAV-mounted RIS. Subsequently, under different compression schemes, two optimization problems are formulated to maximize the weighted sum of the data uploaded by the user equipments (UEs) by jointly designing the WPT and uploading time, the beamforming matrics, the CPU cycles, and the UAV trajectory. A block coordinate descent (BCD) algorithm based on the closed-form beamforming designs and the successive convex approximation (SCA) algorithm is proposed to solve the formulated problems. Furthermore, to highlight the insight of the gains brought by the compression schemes, we analyze the energy efficiencies of compression schemes and confirm that the gains gradually reduce with the increasing power used for compression. Simulation results demonstrate that the amount of collected data can be effectively increased in wireless-powered MCS systems.