An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks

Energy Driven Cluster-Head Rotation (EDCR) class of algorithms are energy aware distributed clustering techniques for effective ad hoc deployed Wireless Sensor Network (WSN) organization. The application of this class of algorithms requires the setting of salient parameters at the design stage of the WSN to achieve desired results. Two such parameters which should be known in advance are the cluster density and the distance between neighbouring cluster heads (CHs). In this research we analyze the effect of algorithm design based on these two parameters. Simulation techniques are given to support and verify the analytical results.

[1]  Christian Bettstetter The cluster density of a distributed clustering algorithm in ad hoc networks , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[2]  Catherine Rosenberg,et al.  A minimum cost heterogeneous sensor network with a lifetime constraint , 2005, IEEE Transactions on Mobile Computing.

[3]  Ossama Younis,et al.  Node clustering in wireless sensor networks: recent developments and deployment challenges , 2006, IEEE Network.

[4]  S. Gamwarige,et al.  A cluster based energy balancing strategy to improve Wireless Sensor Networks lifetime , 2007, 2007 International Conference on Industrial and Information Systems.

[5]  S. Gamwarige,et al.  Application of the EDCR Algorithm in a Cluster Based Multi-hop Wireless Sensor Network , 2006, 2006 International Symposium on Communications and Information Technologies.

[6]  Chris Chatfield,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[7]  Sankalpa Gamwarige,et al.  An Algorithm for Energy Driven Cluster Head Rotation in a Distributed Wireless Sensor Network , 2005 .

[8]  Carol A. Gotway,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[9]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[10]  R. Wolpert,et al.  Likelihood-based inference for Matérn type-III repulsive point processes , 2009, Advances in Applied Probability.

[11]  Sri Lanka A Cluster Based Energy Balancing Strategy to Improve Wireless Sensor Network Lifetime , 2007 .