ELACCA: Efficient Learning Automata Based Cell Clustering Algorithm for Wireless Sensor Networks
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Wireless Sensor Networks (WSNs) are a special type of networks deployed in different geographical regions for capturing the important information. WSNs consist of low energy devices called Sensor Nodes (SNs) which are capable of sensing and transferring the gathered information to remote controller called as Base Stations (BSs). Because these devices are generally deployed in unattended environment and are limited in communication and computing power, so it is not always possible to recharge or replace the batteries for these devices. The SNs are supposed to have self healing and built in intelligence to operate independently. Keeping view of the above, in this paper, we propose a new Efficient Learning Automata based Cell Clustering Algorithm (ELACCA) for WSNs. Compared to the earlier approaches, we have taken size of the cell of the area under investigation in rhombus shape rather than the square. The selection of cluster head (CH) is performed by different levels using the participation ratio of the nodes in respective CH. Using the defined participation ratio, a cut off on number of nodes in a particular CH is also computed. Moreover, by varying the angle from base of the cell to its sides, the numbers of CHs formed are also calculated. Using these values, the communication among different CHs is maintained. The performance of the proposed scheme is validated using the extensive simulation with respect to various parameters such as connectivity, coverage and packet delivery ratio. The results obtained show that the proposed scheme is better than the existing schemes with respect to these metrics.
KeywordsWSNs Clustering Learning automaton
This work was supported by the new faculty research program 2013 of Kookmin University in Korea.
- 1.Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd international conference on embedded networked sensor systems, SenSys ’04. ACM, New York, NY, USA, pp. 95–107.Google Scholar
- 10.Lin, C., Wu, G., Xia, F., Li, M., Yao, L., & Pei, Z. (2012). Energy efficient ant colony algorithms for data aggregation in wireless sensor networks. Journal of Computer and System Sciences, 78(6), 1686–1702.Google Scholar
- 22.Bandyopadhyay, S., & Coyle, E. J. (2013). An energy efficient hierarchical clustering algorithm for wireless sensor networks. In Proceedings of IEEE computer and communications societies (INFOCOM), pp. 1713–1723.Google Scholar
- 23.Wang, Y., Wu, H., Nelavelli, R., & Tzeng, N. F. (2006). Balance based energy-efficient communication protocols for wireless sensor networks. In Proceedings of IEEE international conference workshops on distributed computing systems.Google Scholar
- 25.Attea, B. A., & Khalil, E. A. (2012). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft computing, 12(7), 1950–1957.Google Scholar
- 27.Sarkar, P., & Saha, A. (2011). Security enhanced communication in wireless sensor networks using Reed–Muller codes and partially balanced incomplete block designs. Journal of Convergence, 2(1), 23–30.Google Scholar
- 28.Pan, R., Xu, G., Fu, B., Dolog, P., Wang, Z., & Leginus, M. (2012). Improving recommendations by the clustering of tag neighbours. Journal of Convergence, 3(1), 13–20.Google Scholar
- 29.Silas, S., Ezra, K., & Rajsingh, E.B. (2012). A novel fault tolerant service selection framework for pervasive computing. Human-centric Computing and Information Sciences, 2:5. doi: 10.1186/2192-1962-2-5.
- 30.Dhurandher, S. K., Obaidat, M. S., & Gupta, M. (2012). An acoustic communication based AQUA-GLOMO simulator for underwater networks. Human-centric Computing and Information Sciences, 2:3, 2–14.Google Scholar
- 31.Narendra K. S., & Thathachar, M. A. L. (1980). On the behavior of a learning automaton in a changing environment with application to telephone traffic routing. IEEE Transactions on Systems, Man, and Cybernetics, SMC, l0(5), 262–269.Google Scholar
- 32.Najim, K., & Poznyak, A. S. (1996). Multimodal searching technique based on learning automata with continuous input and changing number of actions. IEEE Transactions on Systems, Man, and Cybernetics-Part B. Cybernetics, 26(4), 666–673.Google Scholar
- 33.Luo, H., Luo, J., Liu, Y., & Das, S. K. (2009). Adaptive data fusion for energy efficient routing in wireless sensor networks. IEEE Transactions on computers, 24(5), 345–359.Google Scholar
- 34.The Network Simulator NS-2. http://www.isi.edu/nsnam/ns/.
- 35.Chandrakasan, A. P., Smith, A. C., & Heinzelman, W. B. (2004). An application specific protocol architecture for wireless micro sensor networks. IEEE Transaction on Wireless Communications, 1(4), 660–669.Google Scholar