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. 2022 Jul 29;22(15):5675.
doi: 10.3390/s22155675.

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

Zhiyi Qu  1   2 , Baoqing Li  1
Affiliations

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

Zhiyi Qu et al. Sensors (Basel). .

Abstract

As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0-1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

Keywords: clustering method; energy efficiency; target tracking; wireless sensor networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Basic cluster structure in the network.
Figure 2
Figure 2
On-demand cluster between static cluster.
Figure 3
Figure 3
Energy model.
Figure 4
Figure 4
Cluster β and its lower approximation region and fuzzy boundary region.
Figure 5
Figure 5
Sensor Activation Scenario (1).
Figure 6
Figure 6
Sensor Activation Scenario (2).
Figure 7
Figure 7
The overlap of the sensing area of two nodes.
Figure 8
Figure 8
Workflow of the proposed algorithm.
Figure 9
Figure 9
Experimental results for average residual energy of sensor nodes.
Figure 10
Figure 10
Experimental results for total energy consumption in the network.
Figure 11
Figure 11
Experimental result for number of activated nodes in the network.
Figure 12
Figure 12
Experimental result for number of energy mean square deviation of nodes.
Figure 13
Figure 13
Experimental result for network lifetime (a) First node dead; (b) Half node dead; (c) Last node dead.
See this image and copyright information in PMC

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