SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks
Joint Authors
Chen, Zhi
Li, Shuai
Yue, Wenjing
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-11
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Zhi& Li, Shuai& Yue, Wenjing. 2014. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks. Journal of Sensors،Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1042908
Modern Language Association (MLA)
Chen, Zhi…[et al.]. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks. Journal of Sensors No. 2014 (2014), pp.1-6.
https://search.emarefa.net/detail/BIM-1042908
American Medical Association (AMA)
Chen, Zhi& Li, Shuai& Yue, Wenjing. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks. Journal of Sensors. 2014. Vol. 2014, no. 2014, pp.1-6.
https://search.emarefa.net/detail/BIM-1042908
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1042908