Charging non-deterministic mobile nodes in a transfer learning approach

Abstract

Wireless rechargeable sensor networks (WRSNs) provide a solution to the energy problem in wireless sensor networks by introducing chargers to recharge the sensor nodes with rechargeable batteries. Most of the existing studies on WRSN focus on charging static nodes or nodes with certain mobility, while a few works investigate charging non-deterministic mobile nodes, where the movement pattern of nodes is unknown. The main challenge in the study of charging non-deterministic nodes is how to find the energy-hungry nodes, because the movement of the nodes is uncertain. The existing schemes assume that the historical trajectories of each node are known and predict the future locations of the nodes by analyzing the trajectory data. However, these schemes require a large amount of trajectory data to train the prediction model, and it takes a considerable amount of time for the network to run to obtain this trajectory data. If there is a node energy depletion during this time, these schemes cannot perform the energy replenishment to the nodes. To address this problem, we propose a novel charging scheme to provide charging services for non-deterministic nodes by introducing the transfer learning technology to predict the future locations of non-deterministic nodes. In the proposed scheme, a backbone Graph Convolutional Network (GCN) is pre-trained with other trajectory datasets, based on which only a small amount of trajectory data of nodes in the target network is needed to complete the location prediction task. In addition, a new scheduling algorithm is proposed, which considers multiple states of the nodes, including the energy consumption rate, the remaining energy state and the future locations, to select the charging target nodes. Simulation results show that the proposed scheme is able to achieve energy replenishment of non-deterministic mobile nodes when the network is just starting to operate.