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Parametric Analysis on Cluster Head Selection using Hybrid Optimization Framework


Kale Navnath Dattatraya, S. Ananthakumaran


Vol. 20  No. 12  pp. 108-114


Clustering and Routing are the most important issues in Wireless Sensor Networks (WSNs) as these factors hold a significant role in data transmission. In clustering, cluster heads (CH) are overloaded with heavy traffic than other nodes of cluster. This leads to the hotspot issues. Therefore, it is essential to choose a suitable CH in a cluster oriented routing model. This paper introduces a novel CH selection model to increase the energy efficiency and life span of network. In addition, this work deploys Fitness based Glowworm swarm with Fruit fly Algorithm (FGF) for the optimal selection of CH. At last, parametric analysis is carried out to prove the supremacy of the presented approach with respect to cost analysis, energy analysis and alive node analysis by varying the count of neighbors and sensor ranges.


Routing; Cluster Head; FGF Algorithm; Clustering; Sensor range.