Open Access

ARTICLE

Enhancing Wireless Sensor Network Efficiency through Al-Biruni Earth Radius Optimization

by Reem Ibrahim Alkanhel¹, Doaa Sami Khafaga², Ahmed Mohamed Zaki³, Marwa M. Eid^4,5, Abdyalaziz A. Al-Mooneam⁶, Abdelhameed Ibrahim⁷, S. K. Towfek^3,*

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Computer Science and Intelligent Systems Research Center, Blacksburg, Virginia, 24060, USA
4 Delta Higher Institute for Engineering and Technology, Mansoura, 35511, Egypt
5 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 35712, Egypt
6 Artificial Intelligence and Computational Intelligence Research Center, Meriden, Connecticut, 06450, USA
7 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, P.O. Box 33349, Isa Town, Bahrain

* Corresponding Author: S. K. Towfek. Email:

Computers, Materials & Continua 2024, 79(3), 3549-3568. https://doi.org/10.32604/cmc.2024.049582

Received 11 January 2024; Accepted 09 April 2024; Issue published 20 June 2024

Abstract

The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

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