| Title |
IoT Network Activation Algorithm in UAV Assisted Wireless Power Transmission Networks using Reinforcement Learning |
| Authors |
김대솔(Daesol Kim) ; 손민정(Minjung Son) ; 하서영(Seoyeong Ha) ; Muy Sengly(Muy Sengly) ; 이정륜(Jungryun Lee) |
| DOI |
https://doi.org/10.5573/ieie.2025.62.3.59 |
| Keywords |
UAV; Reinforcement learning; AI; Replay memory; SWIPT |
| Abstract |
Optimizing the battery use of Internet of Things (IoT) devices to reduce energy waste and maximize the lifespan of devices is one of the important research topics in IoT networks. In this study, we present an algorithm that efficiently activates deactivated IoT terminals in wireless power transmission IoT networks based on simple wireless information and power transfer (SWIPT) radio frequency (RF) communication technology supported by Unmanned Aerial Vehicle (UAV). Based on SWIPT RF communication technology, wireless power transmission UAV is used to charge IoT, and reinforcement learning that optimizes UAV's hovering point and flight path was designed to build a low-power system that minimizes the power used by UAVs that serves as a charging role. By applying the Epsilon Decay policy and the Q-learning algorithm utilizing Replay Memory technology, we present an algorithm that finally determines the flight path of UAV by moving between clusters. As a result of the simulation, it can be confirmed that the agent has been learned effectively. |