Abstract

One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.

Highlights

  • Wireless sensor networks (WSNs) consist of a large amount of small, low-cost, and wirelessly connected sensor nodes deployed in an unattended natural environment

  • This paper proposed a novel distributed and energy-balanced multisensory scheduling strategy

  • This paper proposed a novel distributed and energy-balanced multisensory scheduling strategy for target tracking WSNs

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Summary

Introduction

Wireless sensor networks (WSNs) consist of a large amount of small, low-cost, and wirelessly connected sensor nodes deployed in an unattended natural environment. Target tracking is a classical application in WSNs. Recently, because of sensor technological advances, sensors have become inexpensive and diversified. The use of sensor node with multiple sensors for target tracking has become common. Because the battery of each sensor node is energy limited, it is difficult to replace the battery manually after the network has been deployed. Optimization and balance of the energy cost for multiple sensors have become increasingly important [1,2]

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