@Article{csse.2023.032024,
AUTHOR = {Abeeda Akram, Kashif Zafar, Adnan Noor Mian, Abdul Rauf Baig, Riyad Almakki, Lulwah AlSuwaidan, Shakir Khan},
TITLE = {On Layout Optimization of Wireless Sensor Network Using Meta-Heuristic Approach},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {3},
PAGES = {3685--3701},
URL = {http://www.techscience.com/csse/v46n3/52225},
ISSN = {},
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.},
DOI = {10.32604/csse.2023.032024}
}