Performance Analysis for Efficient Cluster Head Selection in Wireless Sensor Network Using RBFO and Hybrid BFO-BSO
International Journal of Wireless Communications and Mobile Computing
Volume 6, Issue 1, March 2018, Pages: 1-9
Received: Nov. 18, 2017;
Accepted: Nov. 30, 2017;
Published: Jan. 26, 2018
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A. Rajagopal, Department of Electronics & Instrumentation Engineering, Annamalai University, Chidambaram, India
S. Somasundaram, Department of Electronics & Instrumentation Engineering, Annamalai University, Chidambaram, India
B. Sowmya, Department of Electronics & Communication Engineering, Alpha College of Engineering, Chennai, India
Wireless Sensor Network involves in the communication task which demands the devices to form a connected network for collecting and disseminating information through radio transmission. The main objective of the Wireless Sensor Network is to extend the network lifetime in the operational environment, to charge or to exchange the sensor node batteries is probably an impossible/unfeasible activity. The clustered network aims to select CHs that minimize transmission costs and energy. To maximize the network lifetime, optimal CH selection is important. Selections of CH are Non deterministic Polynomial (NP) hard. Recently natural swarm inspired algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) have found their way into this domain and proved their effectiveness. In this work the BFO is adapted for cluster head selection so that multiple objectives like reduced packet delivery ratio, improved cluster formation, improved network life time and reduced end to end delay are achieved. Also a novel Hybrid algorithm using Bacterial foraging Optimization (BFO) - Bee swarm Optimization (BSO) is attempted to analysis the number of clustered formed, end to end delay, packet drop ratio and lifetime.
Performance Analysis for Efficient Cluster Head Selection in Wireless Sensor Network Using RBFO and Hybrid BFO-BSO, International Journal of Wireless Communications and Mobile Computing.
Vol. 6, No. 1,
2018, pp. 1-9.
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