An Efficient Proposed Approach of Dynamic Clustering for Target Tracing in Wireless Sensor Networks

Authors

  • Manas Kumar Ray Lecturer, Department of Computer Application, Banwarilal Bhalotia College, Asansol, West Bengal, India
  • Gitanjali Roy Lecturer, Department of Computer Application, Bidhan Chandra College, Asansol, West Bengal, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.2.1883

Keywords:

Wireless Sensor Network (WSN), Dynamic Clustering, Cluster Head (CH), K Mean Clustering

Abstract

This paper proposes and evaluates decentralized dynamic clustering algorithm for tracing a movable target. Here firstly we proposed dynamic K mean clustering algorithm. In this algorithm a fixed number of sensor nodes is choose and then cluster is created. When the cluster is created then a cluster head (CH) is active. This active CH sensor nodes will create new cluster and that new cluster is also formed a new mean value of cluster head. But, the newly created cluster is only active when a moving objected is trace. According from the position of cluster head, few sensor nodes is active, where as few sensor nodes are inactive. According from the CH nodes newly cluster is created. So, creation of dynamic cluster is less energy efficient and stability of cluster will more than static cluster with sensor nodes. On the other hand, movable object tracing sensor nodes are familiar with energy utilization of sensor nodes. Here we proposed an energy efficient target tracing approach which follow network stability as well as energy saving. As we use dynamic clustering technique, so optimization of energy each sensor nodes with cluster head is maximum. So all the sensors with cluster head sensor nodes will continue more time for object tracing. In simulation result we show that our proposed dynamic K mean clustering algorithm is more accurate and more stable.

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Published

05-08-2018

How to Cite

Ray, M. K., & Roy, G. (2018). An Efficient Proposed Approach of Dynamic Clustering for Target Tracing in Wireless Sensor Networks. Asian Journal of Computer Science and Technology, 7(2), 48–51. https://doi.org/10.51983/ajcst-2018.7.2.1883