An Efficient Proposed Approach of Dynamic Clustering for Target Tracing in Wireless Sensor NetworksAuthor : Manas Kumar Ray and Gitanjali Roy
Volume 7 No.2 July-September 2018 pp 48-51
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.
Wireless Sensor Network (WSN), Dynamic Clustering, Cluster Head (CH), K Mean Clustering
 S. Prakash Kumar and K. S. Ramaswami, “Efficient Cluster Validation with K-Family Clusters on Quality Assessment”, European Journal of Scientific Research, pp.25-36, 2011.
 Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publishers, second Edition, 2006.
 Ye Yingchun, Zhang Laibin, Liang Wei, Yu Dongliang and Wang Zhaohui, “Oil Pipeline Work Conditions Clustering Based on Simulated Annealing K-Means algorithm”, World Congress on Computer Science and Information Engineering, pp. 646-650, 2009.
 Khan, S.S., Ahmad, A., “Cluster center initialization algorithm for kmeans clustering”, Pattern Recognition Letter.25, 2004, pp. 1293–1302.
 M. Chu, H. Haussecker and F. Zhao, “Scalable Information-driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks”, Int’lJ. High Performance Computing Applications, Vol. 16, No. 3, Fall 2002.
 J. Liu, J. Liu, J. Reich, P. Cheung and F. Zhao, “Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications,” Proc. 2nd Workshop on Information Processing in Sensor Networks(IPSN’03), April 2003.
 D. Li, K.Wong, Y. Hu and A. Sayeed, “Detection, Classification, Tracking of Targets in Micro-sensor Networks”, IEEE Signal Processing Magazine, pp. 17-29, March 2002
 X. Sheng and Y-H Hu, “Energy Based Acoustic Source Localization”, Proc. of 2nd Workshop on Information Processing in Sensor Networks (IPSN’03), April 2003.
 F. Zhao, J. Shin and J. Reich, “Information-Driven Dynamic Sensor Collaboration for Tracking Applications”, IEEE Signal Processing Magazine, March 2002.
 Y. Xu and W.C. Lee, “On localized prediction for power efficient object tracking in sensor networks”, in Proc. 1st Int. Workshop Mobile Distrib. Comput., pp. 434–439, 2003.
 G.Y. Jin, X.Y. Lu and M.S. Park, “Dynamic clustering for object tracking in wireless sensor networks”, in Proc. 3rd Int. Symp. UCS, Seoul, Korea, pp. 200–209, 2006.
 W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” in Proc.33rd Hawaii Conf. Syst. Sci., pp. 3005–3014, 2000
 Y. Zhang, L.T. Yang and J. Chen, “RFID and Sensor Networks-Architectures, Protocols, Security, and Integrations; CRC”, USA 2010.
 N. Patwari, J. Ash, S. Kyperountas, A. Hero and R. Moses, “Correal, N. Locating the nodes: Cooperative localization in wireless sensor networks”, IEEE Signal Proc. Mag., Vol. 22, pp. 54–69, 2005.