Open Access

ARTICLE

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

by Manar Ahmed Hamza^1,2,*, Aisha Hassan Abdalla Hashim¹, Dalia H. Elkamchouchi³, Nadhem Nemri⁴, Jaber S. Alzahrani⁵, Amira Sayed A. Aziz⁶, Mnahel Ahmed Ibrahim⁷, Abdelwahed Motwakel²

1 Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, 53100, Malaysia
2 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
3 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
5 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Saudi Arabia
6 Department of Digital Media, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
7 College of Science and Humanities Studies in Alquwayiyah, Shaqra University, Saudi Arabia

* Corresponding Author: Manar Ahmed Hamza. Email:

Computer Systems Science and Engineering 2023, 45(2), 1711-1726. https://doi.org/10.32604/csse.2023.031467

Received 18 April 2022; Accepted 16 June 2022; Issue published 03 November 2022

Abstract

Wireless Sensor Network (WSN) consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment. Designing the energy-efficient data collection methods in large-scale wireless sensor networks is considered to be a difficult area in the research. Sensor node clustering is a popular approach for WSN. Moreover, the sensor nodes are grouped to form clusters in a cluster-based WSN environment. The battery performance of the sensor nodes is likewise constrained. As a result, the energy efficiency of WSNs is critical. In specific, the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station (BS). Therefore, energy efficiency and load balancing are very essential in WSN. In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. The selection of Cluster Heads (CHs) and routing path of every CH from the base station is enhanced by the proposed method. It provides the best routing path and increases the lifetime and energy efficiency of the network. End-to-end delay and packet loss rate have also been improved. The proposed GW-IPSO-TS method enhances the evaluation of alive nodes, dead nodes, network survival index, convergence rate, and standard deviation of sensor nodes. Compared to the existing algorithms, the proposed method outperforms better and improves the lifetime of the network.

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Keywords

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