Optimizing Targets Coverage Quality in UAV-Aided IoT Networks

This article considers maximizing coverage quality in Internet of Things (IoT) networks using unmanned aerial vehicles (UAVs) to augment the link from solar-powered devices to a sink/gateway. Specifically, it aims to jointly optimize the assignment of UAVs to hovering points or a charging station, time in which devices monitor targets, and the amount of data transmitted by devices. These quantities are optimized over a given planning horizon using a mixed integer linear program (MILP). Further, this article presents a heuristic method named decoupled energy aware algorithm (DEAA) to optimize the said quantities. In addition, it outlines a model predictive control (MPC) approach that only requires current and historical energy arrivals information of devices. The simulation results showed that DEAA and MPC achieved 80.58% and 61.19% of the optimal results computed by MILP.