Optimum Path Planning for Large Scale Distributed IoT Data Collection Using a Rechargeable UAV

This paper designs energy-efficient flight trajectories for a rechargeable unmanned aerial vehicle (UAV) with battery capacity limitations, performing data collection from widely distributed internet-of-things (IoT) ground nodes. We propose a novel optimization framework using dynamic programming, incorporating battery recharging as needed. We develop an optimal backward-forward algorithm that jointly determines the service locations for each ground node, the visiting sequence of these locations, and the optimal times to return to a docking station for battery recharging. Our algorithm optimally minimizes the UAV’s total energy consumption, establishing fundamental performance bounds for airborne data harvesting from widely distributed ground nodes.