Open Access

ARTICLE

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

Ahmed S. Almasoud¹, Taiseer Abdalla Elfadil Eisa², Marwa Obayya³, Abdelzahir Abdelmaboud⁴, Mesfer Al Duhayyim⁵, Ishfaq Yaseen⁶, Manar Ahmed Hamza^6,*, Abdelwahed Motwakel⁶

1 Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University,Saudi Arabia
2 Department of Information Systems-Girls Section, King Khalid University, Mahayil, 62529, Saudi Arabia
3 Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Information Systems, College of Science and Artsat Mahayil, King Khalid University, Saudi Arabia
5 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computers, Materials & Continua 2022, 72(2), 3269-3281. https://doi.org/10.32604/cmc.2022.024584

Received 23 October 2021; Accepted 09 February 2022; Issue published 29 March 2022

Abstract

In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does not work with the large –scale wireless sensor networks, therefore it causes reliability and energy consumption problems. To overcome this, the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic (LBC-COFL) algorithm is used. The main objective is to perform the lifespan by balancing the gateways with the load of less energy. The proposed algorithm is evaluated using the metrics such as energy consumption, throughput, central tendency, network lifespan, and total energy utilization.

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Keywords

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