[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(
)
无线传感器网络中基于径向基函数的簇首选择
)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
- 无线传感器网络中基于径向基函数的簇首选择
- Author(s):
- Zhu Xiaorong, Shen Lianfeng
- National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
- 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.
- 采用一种神经网络算法——径向基函数来选择无线传感器网络的节点簇首, 它具有并行处理能力、分布式存储以及快速学习等优点.通过分析得出与节点作为簇首相关的4个因素:节点的剩余能量, 周围分布的节点的数目, 中心度和距离基站的位置.把这4个因素作为神经网络的输入变量, 输出变量就是该节点作为簇首的适应度值.根据网络规模的大小, 基站选出一组作为簇首的节点, 然后广播作为簇首的节点号的消息.如果一个节点被选为簇首, 就向周围广播自己的身份并成立一个新簇, 周围的非簇首节点要求加入该簇并成为它的成员.每簇中由簇首负责管理它的成员并执行数据融合等功能.实验结果表明, 与其他算法相比, 该算法能显著地延长传感器网络的生命.
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.