An energy aware and secure Fuzzy logic based Clustering Algorithm for WSN’S
DOI:
https://doi.org/10.29070/6dvj4t17Keywords:
WSN, Clustering, Fuzzy Logic Based Energy, Trust ManagementAbstract
Concerns about preserving battery life and ensuring data integrity are two of the biggest inwireless sensor networks (WSNs). In addition, the sensor nodes that make up a WSN are themselveslimited in terms of their available energy, therefore it's important that the network as a whole preserve asmuch of its energy as possible. When it comes to the issue of power consumption in WSN, the clusteringmechanism is one of the most important solutions. The current clustering algorithms' biggest flaw is thatthey falsely assume that every node can be trusted equally. In spite of the fact that security is a majorconcern when planning a WSN's architecture, it is often not until clustering that this element of safety isgiven any attention at all. The use of a trust-based mechanism is an interesting strategy for achievingsafe data transfer. As a result, it's important to have a single algorithm that combines trust awarenesswith energy efficiency, i.e. For efficient selection of dependable and energy-efficient node as ClusterLeader, a Fuzzy Logic-based Energy Aware Secure Clustering Algorithm is presented.Downloads
References
Yick, J., Mukherjee, B., &Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.
Al-Karaki, J. N., & Kamal, A. E. (2004). Routing techniques in wireless sensor networks: A survey.IEEE Wireless Communications, 11, 6–28.
Akyildiz, I. F., &Vuran, M. C. (2010). Wireless sensor networks (pp. 131–141). Hoboken: Wiley.
Mhemed, R., Aslam, N., Phillips, W., &Comeau, F. (2012). An energy efficient fuzzy logic cluster formation protocol in wireless sensor networks. Procedia Computer Science, 10, 255– 262
Rajeswari, A.R., Kulothungan, K., Ganapathy, S., &Kannan, A. (2016) Malicious Nodes Detection in MANET Using Back-Off Clustering Approach. Circuits and Systems 7 (8), 2070-2079.
Younis, O., &Fahmy, S. (2004). HEED: A hybrid energy-efficient, distributed clustering approach for Ad Hoc sensor Networks. IEEE Transaction on Mobile Computing, 3, 366–379.
J. Kim, S. Park, Y. Han, T. Chung, CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks, in: Proceedings of the ICACT, 2008, 654– 659
C. Li, M. Ye, G. Chen, J. Wu, An energy-efficient unequal clustering mechanism for wireless sensor networks, in: IEEE International Conference on Mobile Ad Hoc and Sensor Systems Conference, 2005, p. 8
Rajeswari, A.R., Kulothungan, K., Ganapathy, S., &Kannan, A. (2019). Trust Aware Svm Based Ids For Mitigating The Malicious Nodes InManet .International Journal of Innovative Technology and Exploring Engineering .8(8) .185- 197.
Ganapathy, Kulothungan, Yogesh, Kannan, “ A Novel Weighted Fuzzy C-Means Clustering Based on Immune Genetic Algorithm for Intrusion Detection”, Proceeding Engineering, Elsevier, vol. 38, pp. 1750-1757, 2012
Selvi, M., &Nandhini, C., Thangaramya, K., Kulothungan, K., &Kannan, A. (2016). HBO based clustering and energy optimized routing algorithm for WSN. In Proceedings of the eighth international conference on advanced computing (ICoAC) (pp. 89– 92).
Li, F., & Wu, J. (2010). Uncertainty modeling and reduction in MANETs. IEEE Transactions on Mobile Computing, 9(7), 1035–1049.
Wang, X., Ding, L., & Wang, S. (2011). Trust evaluation sensing for wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 60(6), 2088–2095.
Yan, Z., &Prehofer, C. (2011). Autonomic trust management for a component-based software system. IEEE Transactions on Dependable and Secure Computing, 8(6), 810–823.