[1] Zhu Xiaorong, Shen Lianfeng,. RBF-based cluster-head selection for wireless sensor networks [J]. Journal of Southeast University (English Edition), 2006, 22 (4): 451-455. [doi:10.3969/j.issn.1003-7985.2006.04.002]

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RBF-based cluster-head selection for wireless sensor networks()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

22

Issue:

2006 4

Page:

451-455

Research Field:

Information and Communication Engineering

Publishing date:

2006-12-30

Info

Title:

RBF-based cluster-head selection for wireless sensor networks

Author(s):

Zhu Xiaorong;  Shen Lianfeng

National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Keywords:

sensor networks;  radial basis function;  cluster-head selection

PACS:

TN915

DOI:

10.3969/j.issn.1003-7985.2006.04.002

Abstract:

The radial basis function(RBF), a kind of neural networks algorithm, is adopted to select cluster-heads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning.Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy(energy available in each node), number(the number of neighboring nodes), centrality(a value to classify the nodes based on the proximity how central the node is to the cluster), and location(the distance between the base station and the node).The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head.A group of cluster-heads are selected according to the size of network.Then the base station broadcasts a message containing the list of cluster-heads’ IDs to all nodes.After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster.If a node around it receives the message, it registers itself to be a member of the cluster.After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster.Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly.Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.

References:

[1] Krishnamachari B, Estrin D, Wicker S.The impact of data aggregation in wireless sensor networks [A].In:Proc of the 22nd International Conference on Distributed Computing System Workshops[C].Vienna, Austria, 2002.575-578.
[2] Kappler C, Riege G.A real-world, simple wireless sensor network for monitoring electrical energy consumption [A].In:Proc of European Workshop on Wireless Sensor
　　Networks [C].Berlin, Germany, 2004.339-352.
[3] Gupta I, Riordan D, Sampalli S.Cluster-head election using fuzzy logic for wireless sensor networks [A].In:Proc of the 3rd Annual Communication Networks and Services Research Conference[C].Halifax, Canada, 2005.255-260.
[4] Heinzelman W, Chandrakasan A, Balakrishnan H.An application-specific protocol architecture for wireless sensor networks [J].IEEE Transactions on Wireless Communications, 2002, 1(4):660-670.
[5] Intanagonwiwat C, Govindan R, Estrin D, et al.Directed diffusion for wireless sensor networking [J].ACM/IEEE Trans on Networking, 2003, 11(1):2-16.
[6] Liang Q.Cluster-head election for mobile ad hoc wireless network [A].In:Proc of the 14th IEEE International Symposium on Personal Indoor and Mobile Radio Communications [C].Beijing, China, 2003.1623-1628.
[7] Yang Xingjun, Zheng Junli.Artificial neural networks and blind signal processing [M].Beijing:Tsinghua University Press, 2003.42-44.(in Chinese)

Memo

Memo:

Biographies: Zhu Xiaorong(1977—), female, graduate;Shen Lianfeng(corresponding author), male, professor, lfshen@seu.edu.cn.

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