Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Network lifetime is one of the important metrics that indicate the performance of a sensor network. Different techniques are used to elongate network lifetime. Among them, clustering is one of the popular techniques. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most widely cited clustering solutions due to its simplicity and effectiveness. LEACH has several parameters that can be tuned to get better performance. Percentage of cluster heads is one such important parameter which affects the network lifetime significantly. At present it is hard to find the optimum value for the percentage of cluster head parameter due to the absence of a complete mathematical model on LEACH. A complete mathematical model on LEACH can be used to tune other LEACH parameters in order to get better performance. In this paper, we formulate a new and complete mathematical model on LEACH. From this new mathematical model, we compute the value for the optimal percentage of cluster heads in order to increase the network lifetime. Simulation results verify both the correctness of our mathematical model and the effectiveness of computing the optimal percentage of cluster heads to increase the network lifetime.

[1]  Yookun Cho,et al.  PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks , 2007, Comput. Commun..

[2]  Chaewoo Lee,et al.  Energy Modeling for the Cluster-Based Sensor Networks , 2006, The Sixth IEEE International Conference on Computer and Information Technology (CIT'06).

[3]  Will Perkins ON THE STRONG LAW OF LARGE NUMBERS , 2004 .

[4]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[5]  Lei Li,et al.  An energy efficient clustering routing algorithm for wireless sensor networks , 2006 .

[6]  Majid Sarrafzadeh,et al.  Optimal Energy Aware Clustering in Sensor Networks , 2002 .

[7]  Azer Bestavros,et al.  SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks , 2004 .

[8]  Adrian Perrig,et al.  ACE: An Emergent Algorithm for Highly Uniform Cluster Formation , 2004, EWSN.

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

[10]  Elmira Popova,et al.  Renewal reward processes with fuzzy rewards and their applications to T-age replacement policies , 1999, Eur. J. Oper. Res..

[11]  Ossama Younis,et al.  Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach , 2004, IEEE INFOCOM 2004.

[12]  Selvakennedy Selvadurai,et al.  An Energy-Efficient Clustering Algorithm for Multihop Data Gathering in Wireless Sensor Networks , 2006, J. Comput..

[13]  Baoding Liu,et al.  Renewal Process with Fuzzy Interarrival Times and Rewards , 2003, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[14]  Dirk Timmermann,et al.  Low energy adaptive clustering hierarchy with deterministic cluster-head selection , 2002, 4th International Workshop on Mobile and Wireless Communications Network.

[15]  Choon-Sung Nam,et al.  The Adaptive Cluster Head Selection in Wireless Sensor Networks , 2008, 2008 IEEE International Workshop on Semantic Computing and Applications.