An Energy Aware Unequal Clustering Algorithm using Fuzzy Logic for Wireless Sensor Networks
In wireless sensor networks, clustering provides an effective way of organising the sensor nodes to achieve load balancing and increasing the lifetime of the network. Unequal clustering is an extension of common clustering that exhibits even better load balancing. Most existing approaches do not consider node density when clustering, which can pose significant problems. In this paper, a fuzzy-logic based cluster head selection approach is proposed, which considers the residual energy, centrality and density of the nodes. In addition, a fuzzy-logic based clustering range assignment approach is used, which considers the suitability and the position of the nodes in assigning the clustering range. Furthermore, a weight function is used to optimize the selection of the relay nodes. The proposed approach was compared with a number of well known approaches by simulation. The results showed that the proposed approach performs better than the other algorithms in terms of lifetime and other metrics.
Roy, S., Conti, M., Setia, S. & Jajodia, S., Secure Data Aggregation in Wireless Sensor Networks, IEEE Transactions on Information Forensics and Security, 7(3), pp. 1040-1052, 2012.
Tran, K.T.M. & Oh, S.H., Uwsns: A round-based Clustering Scheme for Data Redundancy Resolve, International Journal of Distributed Sensor Networks, 2014.
Xiangning, F. & Yulin, S., Improvement on LEACH Protocol of Wireless Sensor Network, In International Conference on Sensor Technologies and Applications, IEEE, pp. 260-264, 2007.
Heinzelman, W.R., Chandrakasan, A. & Balakrishnan, H., Energy-Efficient Communication Protocol for Wireless Microsensor Networks, In Proceedings of the 33rd annual Hawaii international conference on System sciences, IEEE, 10 pp., 2000.
Heinzelman, W.B., Chandrakasan, A.P. & Balakrishnan, H., An Application-specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, 1(4), pp. 660-670, 2002.
Younis, O. & Fahmy, S., HEED: AHybrid, Energy-efficient, Distributed Clustering Approach for Ad hoc Sensor Networks, IEEE Transactions on Mobile Computing, 3(4), pp. 366-379, 2004.
Suharjono, A. & Hendrantoro, G., A New Unequal Clustering Algorithm using Energy-balanced Area Partitioning for Wireless Sensor Networks, International Journal on Smart Sensing & Intelligent Systems, 6(5), 2013.
Soro, S. & Heinzelman, W.B., Prolonging the Lifetime of Wireless Sensor Networks via Unequal Clustering, In Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium, IEEE, 8 pp., 2005.
Li, C., Ye, M., Chen, G. & Wu, J., An Energy-efficient Unequal Clustering Mechanism for Wireless Sensor Networks, International Conference on Mobile Adhoc and Sensor Systems Conference, IEEE, 8 pp., 2005.
Chen, G., Li, C., Ye, M.& Wu, J., An Unequal Cluster-based Routing Protocol in Wireless Sensor Networks, Wireless Networks, 15(2), pp. 193-207, 2009.
Baranidharan, B., Srividhya, S. & Santhi, B., Energy Efficient Hierarchical Unequal Clustering in Wireless Sensor Networks, Indian Journal of Science and Technology, 7(3), pp. 301-305,2014.
Gupta, I., Riordan, D. & Sampalli, S., Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks, In Proceedings of the 3rd Annual Communication Networks and Services Research Conference, IEEE, pp. 255-260, 2005.
Kim, J.M., Park, S.H., Han, Y.J. & Chung, T.M.,CHEF: Cluster Head Election Mechanism using Fuzzy Logic in Wireless Sensor Networks, In 10th international conference on Advanced communication technology, IEEE, pp. 654-659, 2008.
Song, M.A.O. & Zhao, C.L., Unequal Clustering Algorithm for WSN based on Fuzzy Logic and Improved ACO, The Journal of China Universities of Posts and Telecommunications, 18(6), pp. 89-97, 2011.
Bagci, H., & Yazici, A., An Energy aware Fuzzy Approach to Unequal Clustering in Wireless Sensor Networks, Applied Soft Computing, 13(4), pp. 1741-1749, 2013.
Dutta, R., Gupta, S. & Das, M.K., Low-energy Adaptive Unequal Clustering Protocol using Fuzzy C-Means in Wireless Sensor Networks, Wireless personal communications, 79(2), pp. 1187-1209, 2014. (Journal)
Mao, G., Fidan, B. & Anderson, B.D., Wireless Sensor Network Localization Techniques., Computer networks, 51(10), pp.2529-2553, 2007.
Heinzelman, W.B., Chandrakasan, A.P. & Balakrishnan, H., An Application-Specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, 1(4), pp. 660-670, 2002.
Handy, M.J., Haase, M. & Timmermann, D., Low Energy Adaptive Clustering Hierarchy with Deterministic Cluster-head Selection, In 4th International Workshop on Mobile and Wireless Communications Network, IEEE, pp. 368-372, 2002.
Wang, L.X., A Course in Fuzzy Systems, Prentice-Hall press, USA, 1999.
Roychowdhury, S. & Pedrycz, W., A Survey of Defuzzification Strategies, International Journal of Intelligent Systems, 16(6), pp. 679-695, 2001.
Kawadia, V., & Kumar, P.R., Power Control and Clustering in Ad hoc Networks, In Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), IEEE, pp. 459-469, 2003.
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