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Journal of Sensors
Volume 2014 (2014), Article ID 121278, 6 pages
Research Article

SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

Zhi Chen,1,2,3 Shuai Li,1 and Wenjing Yue1,4

1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
3Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China
4Key Lab of Broadband Wireless Communication and Sensor Network Technology, Ministry of Education, Nanjing 210003, China

Received 23 March 2014; Revised 17 July 2014; Accepted 23 July 2014; Published 11 August 2014

Academic Editor: Andrea Cusano

Copyright © 2014 Zhi Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.