Joint reinforcement learning to optimize multiple UAV charger deployments for individual energy requirement in IoT

Abstract

The technology of wireless power transfer (WPT) utilizing unmanned aerial vehicles (UAVs) presents novel avenues for enhancing the longevity of wireless sensor networks (WSNs), which constitute a critical component of the Internet of Things (IoT). However, existing research on charging deployment generally overlooks the heterogeneous energy requirements within the network, resulting in low charging efficiency for high-energy-consuming nodes. This paper addresses the multiple UAVs optimal cooperative charging deployment problem (MUAVs-OCCDP) and proposes a phased optimization strategy. Firstly, it constructs the network topology and records the energy requirements of the nodes. Based on the strength advantage relationship (SDR), an improved NSGA-II algorithm is designed to generate the initial deployment plan. Then, a two-phase reinforcement learning framework is established: the phase 1 aims to reduce the number of UAVs by optimizing the number of covered nodes and the average charging efficiency; the phase 2 promotes collaboration through the sharing of multi-agent experience and a hybrid reward mechanism to achieve balanced charging energy distribution.