Work

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

Year: 2025

Type: article

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.

Source: Energy and AI

DOI: 10.1016/j.egyai.2025.100622