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

The advancement of Internet of Things technology has made continuous environmental monitoring a crucial re-quirement 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 sens-ing devices can gather the required information, reducing equipment costs and eliminating the inconvenience of wired sensors. This study proposes the utilization of a genetic al-gorithm 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.