Learning Aided Joint Sensor Activation and Mobile Charging Vehicle Scheduling for Energy-Efficient WRSN-Based Industrial IoT

In this paper, the joint sensor activation and mobile charging vehicle scheduling for wireless rechargeable sensor network (WRSN) based industrial Internet of Things (IIoT) is studied. In the proposed framework, an optimal sensor set is selected to collaboratively execute a bundle of heterogeneous industrial tasks (e.g., production-line monitoring), meeting the quality-of-monitoring (QoM) of each individual task, and we consider that a mobile charging vehicle (MCV) is scheduled for recharging sensors before their charging deadlines, i.e., time instants of running out of their batteries, in order to prevent from any potential service interruptions (which is one of the key features of IIoT). Our goal is to jointly optimize the sensor activation and MCV charging scheduling for minimizing the system energy consumption, subject to tasks' QoM requirements, sensor charging deadlines and the energy capacity of the MCV. Unfortunately, solving this problem is nontrivial, because it involves solving two tightly coupled NP-hard optimization problems. To address this issue, we design a novel scheme integrating reinforcement learning and marginal product based approximation algorithms, and prove that it is not only computationally efficient but also theoretically bounded with a guaranteed performance in terms of the approximation ratio. Simulation results show the feasibility of the proposed scheme and demonstrate its superiority over counterparts.

I. Introduction

It Is widely known that wireless sensor network (WSN) is an essential component for enabling the industrial Internet of Things (IIoT) due to its capability of providing pervasive surveillance, control, maintenance and automation in intelligent industrial systems [2]. One one hand, the future IIoT applications may require tremendously high data rates and considerably large processing capacities [3], [4], which will accelerate the energy consumption of sensor devices. On the other hand, geographically distributed sensor devices are typically powered by energy-limited batteries, and once a sensor device runs out of the energy, its perception ability will be greatly reduced and the overall system may collapse. To tackle such sensor energy provisioning problem, researchers studied how to reduce the energy consumption by optimizing sensors deployment [5], wake-up and sleeping scheduling [6], [7], sensing radius adaption mechanism [8], and adapting sensors' sampling rate [9], etc. to prolong the system lifetime. However, these methods cannot fundamentally address the energy shortage, and thus recent advances of wireless power transfer (WPT) technology [10], [11], [12] has inspired the emergence of wireless rechargeable sensor networks (WRSNs) [13]. With the implementation of WRSNs, mobile charging vehicles (MCVs) equipped with powerful transceivers can be dispatched to travel around and replenish the energy of sensors via coupled magnetic resonance.