Energy-balanced path optimization of UAV-assisted wireless power and information system

Abstract

The unmanned aerial vehicle (UAV)-assisted wireless power and information system is one of the great choices for energy supplement and information collection of the wireless sensor network (WSN). The ground wireless sensors are operating by the harvested radio frequency (RF) energy from UAVs. The lifetime of the wireless sensor network is affected by the minimal, mean and variance of harvested energy. In this paper, an energy-balanced path optimization of a single UAV is proposed. The proposed framework (1) utilizes the Lagrangian method and an energy threshold to obtain a relaxation solution without maximum speed constraint, (2) implements a genetic algorithm and continuous convex optimization algorithm to obtain optimal trajectory and power allocation strategy. Numerical results show that the minimal, average and variance of harvested energy of wireless sensors are improved under the different distributions of sensors. Based on the proposed framework, the minimal operational requirement of the UAV could be used to guide the model selection of the UAV.