ملخص المقالة :
An efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. The implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. According to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses Fuzzy Inference Systems to select cluster head. The algorithm not only balances the energy load of all nodes, but also provides a reliable selection of a new cluster head and optimality routing for the whole networks. Simulation results demonstrate that the proposed algorithm effectively increases the accuracy to select a cluster head and prolongs the network lifetime
المصادر:
[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless sensor networks: a survey”, Computer networks, Vol.38, No.4, pp.393-422, 2002.
[2] J. Yick, B. Mukherjee and D. Ghosal, “Wireless sensor networks Survay”, Computer networks, Vol.52, No.12, pp.2292-2330, 2008.
[3] X. Fu and Z. Yu, “A reliable and efficient clustering algorithm for WSNs using Fuzzy Petri Nets”, IEEE Conference on 6th International In Wireless Communications Networking and Mobile Computing, pp.1-4, 2010.
[4]
A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks”, Computer communications, Vol.30, No.14, pp.2826-2841, 2007.
[5] W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks”, IEEE Transactions on Wireless Communications, Vol.1, No.4, pp.660-670, 2002.
[6] S. Lindsey and C. S. Raghavendra, “PEGASIS: Power efficient gathering in sensor information systems”, IEEE In Aerospace conference proceedings, Vol.3, pp.1125-1130, 2002.
[7] O. Younis and S. Fahmy, “HEED: a hybrid, energy efficient, distributed clustering approach for adhoc sensor networks”, IEEE Transactions on Mobile Computing, Vol.3, No.4, pp.366-379, 2004.
[8] J. Yen, “Fuzzy logic-a modern perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol.11, No.1, pp.153-165, 1999.
[9] S. N. Sivanandum, S. Sumathi and S. N. Deepa, “Introduction to fuzzy logic using MATLAB”, Springer, 2007.
[10] I. Gupta, D. Riordan and S. Sampalli, “Cluster-head election using fuzzy logic for wireless sensor networks”, IEEE Conference In Communication Networks and Services Research, Proceedings of the 3rd Annual, pp.255-260, 2005.
[11] L. Lao and J. H. Cui, “Reducing multicast traffic load for cellular networks using ad hoc networks”, IEEE Transactions on Vehicular Technology, Vol.55, No.3, pp.822-830, 2006.
[12] T. C. Chiang, C. F. Tai and T. W. Hou, “A knowledge-based inference multicast protocol using adaptive fuzzy Petri nets”, Expert Systems with Applications, Vol.36, No.4, pp.8115-8123, 2009.
[13] H. Karl and A. Willing, “Protocols and architectures for wireless sensor networks”, John Wiley & Sons, 2005.
[14] Y. Qu, J. Fang and S. Zhang, “Identifying Neighbor and Connectivity of Wireless sensor networks with Poisson Point Process”, Wireless Personal Communications, Vol.64, No.4, pp.795-809, 2012
