Open Access

ARTICLE

On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach

by Abeeda Akram¹, Kashif Zafar¹, Adnan Noor Mian², Abdul Rauf Baig³, Riyad Almakki³, Lulwah AlSuwaidan³, Shakir Khan^3,4,*

1 National University of Computer and Emerging Sciences, Islamabad (Lahore Campus), 44000, Pakistan
2 Information Technology University, Lahore, Pakistan
3 Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
4 University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India

* Corresponding Author: Shakir Khan. Email:

Computer Systems Science and Engineering 2023, 46(3), 3685-3701. https://doi.org/10.32604/csse.2023.032024

Received 04 May 2022; Accepted 04 August 2022; Issue published 03 April 2023

Abstract

One of the important research issues in wireless sensor networks (WSNs) is the optimal layout designing for the deployment of sensor nodes. It directly affects the quality of monitoring, cost, and detection capability of WSNs. Layout optimization is an NP-hard combinatorial problem, which requires optimization of multiple competing objectives like cost, coverage, connectivity, lifetime, load balancing, and energy consumption of sensor nodes. In the last decade, several meta-heuristic optimization techniques have been proposed to solve this problem, such as genetic algorithms (GA) and particle swarm optimization (PSO). However, these approaches either provided computationally expensive solutions or covered a limited number of objectives, which are combinations of area coverage, the number of sensor nodes, energy consumption, and lifetime. In this study, a meta-heuristic multi-objective firefly algorithm (MOFA) is presented to solve the layout optimization problem. Here, the main goal is to cover a number of objectives related to optimal layouts of homogeneous WSNs, which includes coverage, connectivity, lifetime, energy consumption and the number of sensor nodes. Simulation results showed that MOFA created optimal Pareto front of non-dominated solutions with better hyper-volumes and spread of solutions, in comparison to multi-objective genetic algorithms (IBEA, NSGA-II) and particle swarm optimizers (OMOPSO, SMOPSO). Therefore, MOFA can be used in real-time deployment applications of large-scale WSNs to enhance their detection capability and quality of monitoring.

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Keywords

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