Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

Abstract

A primary criterion of wireless sensor network is energy efficiency. Focused on the energy problem of target tracking in wireless sensor networks, this paper proposes a cluster-based dynamic energy management mechanism. Target tracking problem is formulated by the multi-sensor detection model as well as energy consumption model. A distributed adaptive clustering approach is investigated to form a reasonable routing framework which has uniform cluster head distribution. Dijkstra's algorithm is utilized to obtain optimal intra-cluster routing. Target position is predicted by particle filter. The predicted target position is adopted to estimate the idle interval of sensor nodes. Hence, dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that the operation energy consumption of wireless sensor network can be reduced. The sensor nodes around the target wake up on time and act as sensing candidates. With the candidate sensor nodes and predicted target position, the optimal sensor node selection is considered. Binary particle swarm optimization is proposed to minimize the total energy consumption during collaborative sensing and data reporting. Experimental results verify that the proposed clustering approach establishes a low-energy communication structure while the energy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energy management.

A Distributed Energy-Efficient Algorithm for Cellular Target Tracking in Wireless Sensor Networks

“…As one of the sleep scheduling approaches, proactive wake-up has been studied for awakening sensors pro-actively to prepare for the approaching target [3,4]. Wang et al…”

“…Idle listening is a major source of energy waste for target detection and measurement [1]. In particular, sleep scheduling is one of the most commonly-used mechanisms for reducing the energy consumption during idle listening in order to extend the network lifetime [3]. The idea of sleep scheduling is to put sensors in the sleep state for most of the time and only wake them up periodically.…”

Energy efficient radio tomographic imaging

“…In contrast to energy efficiency in RF sensor networks, research in energy efficiency in wireless sensor networks (WSN) is significantly more mature [28], [29], [30]. A general WSN uses transmission and reception purely for data communication, not to measure the environment as in an RF sensor network.…”

A Bio-Inspired Distributed Clustering Algorithm for Wireless Sensor Networks

“…As discussed above, distributed topology control is the only option for large-scale sensor networks. Most existing clustering algorithms are designed to achieve certain distributive-ness and can be classified into two main categories: datatransmission-oriented, which focus on inter-and intra-cluster routing to improve energy efficiency [4,8,19,24]; and applicationoriented, which aim at efficient target detection, tracking, and information aggregation [9,12,21]. There are also some algorithms that jointly consider both of these two aspects via heuristic approaches [20,26].…”