An energy-balanced multi-sensor scheduling scheme for collaborative target tracking in wireless sensor networks

Pengcheng Fu,Baoqing Li,Yongbo Cheng,Xiaobing Yuan,Hanwang Qian,Hongying Tang

doi:10.1177/1550147717698968

Abstract

Improving tracking quality and extending network lifespan are two main objects for target tracking, which are usually contradictory due to limited energy of sensor nodes in wireless sensor networks. This article incorporates this contradiction into a problem of multi-objective optimization in tracking networks where multiple sensor nodes are scheduled for collaborative target tracking by adopting the unscented Kalman filter algorithm. We propose an effective scheme to extend the lifespan of wireless sensor networks while guaranteeing preset tracking quality. More specifically, with regard to practical circumstances, we perform analysis on the target detection probability, as well as residual energy of sensor nodes, when selecting a suitable set of candidate sensor nodes. Then, we put forward a novel energy-balanced sensor nodes scheduling algorithm, Greedy Balance Replace Heuristic Algorithm, to select a near-optimal task sensor set from the candidate sensor node set to balance tracking quality and network lifetime. In addition, we also design an efficient multi-sensor node collaborative method to track a single target and to timely report its state to the remote end. From simulation results, it is demonstrated that the proposed node scheduling scheme can not only maintain the preset tracking accuracy but also extend network lifespan with a low computation complexity.

Highlights

Wireless sensor networks (WSNs) have long been recognized as an important information-gathering approach for many applications, such as environmental monitoring, security surveillance, and industry control.[1,2,3,4] WSNs are ordinarily composed of a large number of tiny low-cost, energy-limited, and sensing range-limited sensor nodes
The basic idea of this method is that the multi-objective programming problem is transformed into the single-objective programming problem, which is constructed by each objective function of the multiobjective programming problem
The vector f0 = 1⁄2Eavg(jÃ1), Be(jÃ2), which consists of the sub-optimal solution Eavg(jÃ1) and the sub-optimal solution Be(jÃ2), does not belong to the image set of the double-objective programming problems

Summary

Introduction

Wireless sensor networks (WSNs) have long been recognized as an important information-gathering approach for many applications, such as environmental monitoring, security surveillance, and industry control.[1,2,3,4] WSNs are ordinarily composed of a large number of tiny low-cost, energy-limited, and sensing range-limited sensor nodes. In terms of the existence of interferences in sensor readings and restrictions on power supply and communication competence, it is a challenge to develop an energy-efficient and accurate target-tracking technique in WSNs. In other words, improving tracking quality and extending network lifetime are two conflicting requirements. With limited battery power, not all sensor nodes contribute for tracking target. How to select appropriate task nodes at each timestep is of critical importance both for extending network lifespan and guaranteeing tracking quality

Objectives

Results

Conclusion