Enhanced Flower Pollination-Based Energy Aware Clustering Scheme for Lifetime Maximization in IoT-Enabled Wireless Sensor Networks

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 7

Year of Publication : 2023

Authors : A. Gayathri, S. Sasikumar, R. Yalini

10.14445/23488379/IJEEE-V10I7P106

How to Cite?

A. Gayathri, S. Sasikumar, R. Yalini, "Enhanced Flower Pollination-Based Energy Aware Clustering Scheme for Lifetime Maximization in IoT-Enabled Wireless Sensor Networks," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 7, pp. 63-75, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I7P106

Abstract:

Wireless Sensor Network (WSN) based on the Internet of Things (IoT) involves the process of transmitting the data acquired by sensors mounted on the Sensors Node (SN) to the Base Station (BS). WSN Lifespan is highly dependent on the higher battery life or energy of SNs resulting in a longer lifespan. The WSN sustained operation can be attained with the efficient utilization of SN energy. Clustering stands as one popular technique for increasing the WSN's lifespan. The optimum number of Cluster Heads (CHs) and the way of organizing the clusters were the main problems that needed to be solved in the clustering approaches. This study develops an Enhanced Flower Pollination based Energy Aware Clustering Scheme for Lifetime Maximization (EFPB-EACSLM) in IoT-enabled WSN. The core aim of the EFPB-EACSLM methodology is to properly construct the clusters in WSN and effectively identify the CHs in the network. In the presented EFPB-EACSLM methodology, the first-order radio energy model was exploited. Besides, the EFPB-EACSLM model calculates a Fitness Function (FF) so that energy consumption is mitigated and the lifespan is increased. For validating the performance of the EFPB-EACSLM model, numerous simulation analyses are carried out and the experimental outcomes are compared with current methods. The gained outcomes portrayed the superior performance of the EFPB-EACSLM technique through diverse measuring.

Keywords:

Flower Pollination Algorithm, Internet of Things, Wireless Sensor Networks, Clustering, Energy efficiency, Lifetime maximization.

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