Abstract—The structure of the wireless sensor network for energy management is an investigating area of research since the power resource of the sensor nodes is considered as a battery. Clustering-based methods are introduced through information aggregation to stabilize energy utilization for efficient communication amid the nodes of sensor networks. Clustering is the technique of splitting the sensing region into a number of sensor groups and allocating a leader node (Cluster Head) for that group. To enhance the search efficiency and optimal coverage the Squirrel search algorithm (SSA) is offered for cluster head election. SSA mimics the energetic searching and gliding behavior of flying squirrels (FSs). The specialty of SSA like Gliding, Seasonal monitoring condition and Predator presence probability overcomes the inconsistent tradeoffs between exploration-exploitation and global search constraints of the existing meta-heuristics algorithm. The network's performance is analyzed in terms of the overall lifespan of the nodes. The simulation results show the proposed SSA provides an improvement in residual energy and throughput by 77.66% and 28.60% respectively, than the PSO algorithm. 
 

Index Terms—Cluster head selection, SSA, wireless sensor network, clustering

Cite: N. Lavanya and T. Shankar, "Energy Efficient Cluster Head Selection Using Squirrel Search Algorithm in Wireless Sensor Networks," Journal of Communications vol. 15, no. 6, pp. 528-536, June 2020. Doi: 10.12720/jcm.15.6.528-536

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