Wireless Charging for Uncertain Location Nodes

Benefiting from Wireless Power Transfer (WPT) technology, Wireless Rechargeable Sensor Networks (WRSNs) effectively address the lifetime bottleneck of sensor nodes, enabling them to work perpetually. Most state-of-the-art studies assume that all WRSNs’ information is known or precise in advance. However, sensor nodes may be deployed randomly in a large-scale area, and some critical information (such as node location) may be unavailable or difficult to obtain precisely. In this work, we eliminate the effect of uncertain or imprecise node location and formalize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>M</u>aximizing <u>C</u>harging <u>E</u>nergy utility for uncertain location nodes</i> problem (i.e., MCE problem). With magnetic resonance coupling and beamforming technologies, we propose a novel node localization method to determine precise node location information. In addition, we present a reinforcement learning framework and a charging path scheduling method to maximize charging energy. To validate the effectiveness of our proposed scheme in real-world scenarios, we conduct test-bed experiments. The results demonstrate that our approach significantly improves charging efficiency by an average of 20.9% in a large-scale network, even when the locations of sensors are entirely unknown.