Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.

[1]  Constantin Zopounidis,et al.  Multicriteria classification and sorting methods: A literature review , 2002, Eur. J. Oper. Res..

[2]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[3]  S. M. Sapuan,et al.  A comprehensive VIKOR method for material selection , 2011, Materials & Design.

[4]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[5]  Rahul Vaish,et al.  Pareto Optimal Microwave Dielectric Materials , 2013 .

[6]  Ramjee Prasad,et al.  Application Oriented Multi Criteria Optimization in WSNs Using on AHP , 2012, Wirel. Pers. Commun..

[7]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[8]  Javier Bajo,et al.  Using Heterogeneous Wireless Sensor Networks in a Telemonitoring System for Healthcare , 2010, IEEE Transactions on Information Technology in Biomedicine.

[9]  Stelios H. Zanakis,et al.  Multi-attribute decision making: A simulation comparison of select methods , 1998, Eur. J. Oper. Res..

[10]  Vidushi Sharma,et al.  Maximum Residual Energy Based Clustering Scheme for Wireless Sensor Networks , 2013 .

[11]  Cem Ersoy,et al.  Detection quality of border surveillance wireless sensor networks in the existence of trespassers' favorite paths , 2012, Comput. Commun..

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

[13]  Ian F. Akyildiz,et al.  On network connectivity of wireless sensor networks for sandstorm monitoring , 2011, Comput. Networks.

[14]  Vidushi Sharma,et al.  Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment , 2013 .

[15]  Shyamala C. Sivakumar,et al.  Energy Conserving Architectures and Algorithms for Wireless Sensor Networks , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[16]  K. Lewis,et al.  Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method , 2001 .

[17]  R. B. Patel,et al.  EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks , 2009, Comput. Commun..

[18]  Dirk Pesch,et al.  InRout - A QoS aware route selection algorithm for industrial wireless sensor networks , 2012, Ad Hoc Networks.

[19]  Chung-Horng Lung,et al.  Using Hierarchical Agglomerative Clustering in Wireless Sensor Networks: An Energy-Efficient and Flexible Approach , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[20]  Özgür Ulusoy,et al.  A framework for use of wireless sensor networks in forest fire detection and monitoring , 2012, Comput. Environ. Urban Syst..

[21]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[22]  Rencheng Jin,et al.  Energy-Efficient Cluster Head Selection Scheme Based on Multiple Criteria Decision Making for Wireless Sensor Networks , 2010, Wireless Personal Communications.