Cluster Head Selection Optimization Based on Genetic Algorithm to Prolong Lifetime of Wireless Sensor Networks

Abstract Wireless sensor networks gain ample interest because of their wide range of applications. Efficient energy consumption of nodes is the prime design issue for these networks. Clustering approaches prolong the network lifetime with the load balanced network. To achieve load balancing clustering algorithm rotate the role of cluster head among the nodes so, cluster head selection process is pivotal for clustering algorithms. Work of this paper presents a genetic algorithm based cluster head selection for centralized clustering algorithms to have a better load balanced network than the traditional clustering algorithm. Simulation shows that the proposed solution finds the optimal cluster heads and has prolonged network lifetime than the traditional clustering algorithms.

[1]  Vinayak S. Naik,et al.  A line in the sand: a wireless sensor network for target detection, classification, and tracking , 2004, Comput. Networks.

[2]  Wendi B. Heinzelman,et al.  Application-specific protocol architectures for wireless networks , 2000 .

[3]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[4]  Pranesh V. Kallapur,et al.  Clustering in Wireless Sensor Networks: Performance Comparison of LEACH & LEACH-C Protocols Using NS2 , 2012 .

[5]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[6]  Irfan-Ullah Awan,et al.  Adaptive decentralized re-clustering protocol for wireless sensor networks , 2011, J. Comput. Syst. Sci..

[7]  Pramod K. Varshney,et al.  Data-aggregation techniques in sensor networks: a survey , 2006, IEEE Communications Surveys & Tutorials.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  Fernando J. Velez,et al.  Application of Wireless Sensor Networks to Automobiles , 2008 .

[10]  Girdhari Singh,et al.  Network adaptive round-time clustering algorithm for wireless sensor networks , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[11]  Hisao Ishibuchi,et al.  Performance evaluation of genetic algorithms for flowshop scheduling problems , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[12]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[13]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[14]  Andreas Willig,et al.  Protocols and Architectures for Wireless Sensor Networks , 2005 .

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

[16]  Neeraj Kumar,et al.  A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks , 2013, J. Netw. Comput. Appl..

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

[18]  Rajeev Tripathi,et al.  Optimal number of clusters in wireless sensor networks: An FCM approach , 2010, 2010 International Conference on Computer and Communication Technology (ICCCT).

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

[20]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.