An Efficient Combined Charging Strategy for Large-Scale Wireless Rechargeable Sensor Networks

The adoption of mobile energy equipment to replenish the energy in wireless sensor networks is gaining increasing attention. However, pioneering work on charging neglects the location and time-varied energy feature of the node, leading to a high charging frequency and long charging path. In this work, we aim to reduce the charging frequency and improve its efficiency. The temporal-spatial combined charging strategy (TSCCS) is developed, which can guarantee that a network runs for a long time. First, an energy and location aware K-means (ELAK-means) clustering algorithm is proposed to group nodes into clusters and construct the spatial location and the energy consumption of nodes. Second, to balance the energy consumption of nodes, an intra-cluster communication mode-simple tree-like communication mode (STCM) is proposed. A multiple backtracking method (M-BTM) ensures the communication mode of each node. The communication hops of nodes can be limited to within two hops in a cluster. Simulations demonstrate that TSCCS can enhance performance by reducing journey length and average charging frequency, improving the charging efficiency and ensuring that the energy consumption is more balanced across the network.

I. Introduction

Future wireless networks will include many innovative and unconventional services, such as machine-type communications, IoT applications, and haptic communications [1]. Wireless sensor networks (WSNs) using perception technology have been extensively applied in scenarios such as manufacturing, military surveillance, smart cities, and disaster forecasting [2]. WSNs rely on ubiquitous tiny nodes with limited energy to capture multidimensional data from surrounding objects. Due to the limited battery capacity of the node, energy is a major bottleneck in improving the lifetime of WSNs [3]. Energy conservation schemes such as energy-aware routing [4], data fusion [5], and medium access control (MAC) layers [6] have been proposed to address this energy constraint. However, each node is limited by the capacity of its onboard battery, and sensor lifetime prolongation remains a critical issue.