Importance-Different Charging Scheduling Based on Matroid Theory for Wireless Rechargeable Sensor Networks

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

I. Introduction

Energy shortage is one of the most thorny issues in wireless sensor networks (WSNs), owing to the limited battery capacities of sensor nodes. Energy consumption is also a serious problem in the future sixth generation (6G) networks [1]. Recently the breakthrough in wireless power transfer technique has offered a significant alternative to replenish such sensor nodes [2], spawning the widespread applications of wireless rechargeable sensor networks (WRSNs) [3], [4]. As contrasted with other energy saving strategies, such as load balancing techniques [5]–[8], the energy shortfall problem has been better addressed by the energy replenishment. Thus, for long-term surveillance applications, WRSNs are more attractive than traditional WSNs [9]. Wireless energy transfer consists of two types: energy harvesting from the nature (e.g., wind power) and wireless charging through mobile equipments (e.g., robots). The former is more economical, while the latter can furnish more stable and reliable energy resource [10], [11].