Unequal Clustering by Improved Particle Swarm Optimization in Wireless Sensor Network | Zendy

Solmaz Salehian | Zendy; Shamala. K. Subraminiam | Zendy

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Unequal Clustering by Improved Particle Swarm Optimization in Wireless Sensor Network

Author(s) -

Solmaz Salehian,

Shamala. K. Subraminiam

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.08.433

Subject(s) - computer science , wireless sensor network , particle swarm optimization , cluster analysis , partition (number theory) , energy consumption , cluster (spacecraft) , distributed computing , swarm behaviour , network partition , network performance , computer network , mathematical optimization , data mining , algorithm , machine learning , artificial intelligence , ecology , mathematics , combinatorics , biology

A significant design issue in Wireless Sensor Networks (WSNs) is to reduce energy consumption or to ensure its usage is organized and managed in the best possible manner. In this research Improved Particle Swarm Optimization (IPSO) is adopted in Energy-Balanced Unequal Clustering (EBUC) with the aim of optimize performance in terms of the number of alive nodes in WSNs. The performance of the adopted IPSO algorithm are validated by Numerical experiments in conventional background, however it has not been deployed in cluster-based WSNs which is done by this research. The IPSO is used to form clusters and partition the network in cluster-based WSNs with objective of addressing the standard PSO issues, which degrade the performance. The acquired results showed that the deploying IPSO in EBUC algorithm decreases the number of the dead nodes and increase network life time

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