UAV-Assisted Mobile Crowdsensing Systems Empowered by Wireless Power Transfer and Adaptive Compression Techniques

Mobile crowdsensing (MCS) and wireless power transfer (WPT) are two promising paradigms to enhance sensing coverage and prolong the operational time of users. However, current research on the MCS and the unmanned aerial vehicle (UAV)-enabled WPT faces two challenges, namely, neglecting the impact of the computing capabilities of users on resource allocation and employing an omnidirectional energy transmission model. To address the above challenges, we propose a UAV-assisted data collection framework that considers both the adaptive compression of users influenced by environmental factors and the directional energy transmission model. We formulate a problem of maximizing the amount of collected data of the UAV under the proposed framework by jointly optimizing the compression rate, the WPT time, the uploading time, the transmit power of users, and the UAV trajectory. To tackle the formulated non-convex optimization problem, we propose an iterative algorithm using the multi-variable decoupling method and the successive convex approximation (SCA) method. Simulation results verify that the proposed approach is more efficient than the existing scheme without data compression.