Paper Info
Title
A Hybrid Swarm Intelligence Based Optimization Approach For Solving Minimum Exposure Problem In Wireless Sensor Networks
Abstract
Minimum Exposure Problem (MEP) has become one of the most important issues in WSN as it affects the coverage quality measurement to a greater extent. Recently, MEP issues have been dealt by Physarum Optimization Algorithm (POA). Nevertheless, this method ends with the less scope in precision faults. Along with these issues, the necessity for computation time and memory requirements gets increases statistically. The major aim of the work is to propose a new optimization algorithm to solve the MEP issue. Thus, Hybrid Genetic Particle Swarm Optimization (H-GPSO) is formulated to give a desirable solution for MEP issue. In the proposed work, energy usage of the sensor node for MEP identification is measured using Hidden Markov Model (HMM) with the intention of prolonging the lifetime of the sensor node. After accomplishing the energy efficiency, MEP is developed and converted to an optimization issue. H-GPSO is presented to resolve the optimization problem; hence, it gives a desirable solution to MEP issue. Therefore, the pretended answer proves the proposed H-GPSO related MEP model is desirable for detecting the minimal exposure problem with high energy ratio. The results of the proposed and existing methods are measured in terms of Energy, Throughput, Delay, and Exposure via NS2.
Year
DOI
Venue
2021
10.1002/cpe.5370
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
hidden Markov model, hybrid genetic particle swarm optimization, minimum exposure problem, physarum optimization algorithm
Journal
33
Issue
ISSN
Citations 
3
1532-0626
2
PageRank 
References 
Authors
0.36
0
4
Name
Order
Citations
PageRank
S.S. Aravinth120.36
J. Senthilkumar2216.28
V. Mohanraj3186.46
Y. Suresh4214.25