Open Access Open Access  Restricted Access Subscription or Fee Access

Compare of Static and Dynamic Energy Efficient Clustering Techniques Using Fuzzy Logic in Wireless Sensor Network

Pooja devi, Laxmi Shrivastava

Abstract


A wireless device network (WSN) often checks physical or environmental conditions and sends the collected information to a base station (BS) through network. In WSN, clustering is wide used technique for protracting network period of time as clustering is economical technique for reducing energy dissipation. Clustering joins along many device nodes to create cluster and performs election for choosing Cluster Head (CH) for all the clusters. In clustering technique CH choice plays necessary role as major functions of CH is to stay information of attached device nodes and to communicate with Cluster Head of different clusters. During this paper, have a tendency propose a new routing approach known as Energy economical fuzzy logic cluster head (EEFL-CH); it is associate in nursing improvement of LEACH protocol. The goal of this approach is to decrease energy consumption in terms of network period of time extension using three fuzzy parameters. These parameters area unit residual energy, expected potency, and closeness to base station.

Full Text:

PDF

References


. W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy efficient communication protocol for wireless micro sensor networks, In: Proceedings of the 33rd Hawaii International Conference on System Sciences. 2000, 3005–14p.

. S. Lindsey, C.S. Raghavendra. PEGASIS power-efficient gathering in sensor information systems, IEEE Aerospace Conf Proc. 2002; 1125–30p.

. J.N. Al-Karaki, I.T. Al-Malkawi. On energy efficient routing for wireless sensor networks, Innov Inform Technol. 2008; 465–9p.

. W.B. Heinzelman, A.P. Chandrakasan, H. Balakrishnan. An application-specific protocol architecture for wireless micro sensor networks, Wireless Commun IEEE Trans. 2002; 1(4): 660–70p.

. Q. Liang, L. Wang. Redundancy Reduction in Wireless Sensor Networks Using SVD-QR. MILCOM, Atlantic City, NJ, 2005.

. Q. Ren, Q. Liang. A Hybrid Approach for Asynchronous Energy-Efficient MAC Protocol for Wireless Sensor Networks. Globecom, St Louis, MO, 2005.

. Q. Ren, Q. Liang. Energy-Efficient Query in Sensor Database Systems with Uncertainties. MILCOM, Atlantic City, NJ, 2005.

. L. Zhao, Q. Liang. Fuzzy deployment for wireless sensor networks, IEEE Computational Intelligence on Homeland Security and Personal Safety. Orlando, FL, March 2005.

. L. Zhao, Q. Liang. Clustering with Fuzzy Cluster Radius. Globecom, St Louis, MO, 2005.

. E. Ahvar, A. Pourmoslemi, M. JalilPiran. FEAR: a fuzzy-based energy-aware routing protocol for wireless sensor networks, Int J Grid Comput Appl (IJGCA). 2011; 2(2).

. I. Gupta, D. Riordan, S. Sampalli. Cluster-head election using fuzzy logic for wireless sensor networks, In: Communication Networks and Services Research Conference, 2005. Proceedings of the 3rd Annual. IEEE, 2005.

. J.-M. Kim, et al. CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks, Adv Commun Technol. 2008. ICACT 2008. 10th International Conference on. Vol. 1. IEEE, 2008.

. J.-S. Lee, W.-L. Cheng. Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication, Sensors J IEEE. 2012; 12.9: 2891–7p.

. H. El Alami, A. Najid. SEFP: a new routing approach using fuzzy logic for clustered heterogeneous wireless sensor networks, Int J Smart Sens Intell Syst. 2015; 8.9: 2286–306p.




DOI: https://doi.org/10.37628/jbcc.v3i1.577

Refbacks

  • There are currently no refbacks.