Efficient Charging and Data Collection in UAV-Aided Backscatter Sensor Networks

Backscatter communication based wireless charging of the sensor nodes and data collection from them is a promising solution due to ultra-low power consumption. However, challenges of short transmission range requirement, high self-interference, and simultaneous operation with multiple backscatter nodes (BSNs) need to be addressed. To this end, this paper presents a novel framework for joint field data collection and wireless charging in an unmanned aerial vehicle (UAV)-aided wireless sensor network via monostatic backscatter communication at millimeter waves. The framework is divided into three tasks, namely, energy-optimized UAV transceiver design, UAV constraints aware BSN clustering, and optimized resource allocation per cluster. To strike a balance between serving efficiency and self-interference, optimum BSN cluster size is estimated offline, which in turn governs BSN clustering optimization. With UAV communication energy and clustering information, a joint sum energy transfer and sum data collection maximization problem is formulated by considering the minimum required charging and data collection constraints. To handle non-convexity, an alternating optimization approach is devised, estimating optimal backscatter reflection coefficients, data collection time, and power distribution among the BSNs using successive convex approximation. Finally, via Monte-Carlo simulations, performance of the proposed system is compared with the current state-of-the-art.