GA-based Charger Deployment Algorithm in Indoor Wireless Rechargeable Sensor Networks

Abstract

The advancement of Internet of Things technology has made continuous environmental monitoring a crucial requirement for various applications. To address the issue of the network life cycle, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution. This research is focused on WRSN applications in indoor settings such as the Industrial Internet of Things and indoor greenhouses. In such scenarios, rechargeable wireless sensing devices can gather the required information, reducing equipment costs and eliminating the inconvenience of wired sensors. This study proposes the utilization of a genetic algorithm to optimize the deployment of chargers in indoor environments. Compared to greedy algorithms, this approach can determine the best solution for charger deployment and minimize deployment expenses.