Binary Monkey-King Evolutionary Algorithm for single objective target based WSN
DOI:
https://doi.org/10.4108/eai.29-7-2019.163970Keywords:
Single objective WSN, Genetic Algorithm, Particle Swarm Optimization, Monkey King Evolution AlgorithmAbstract
INTRODUCTION: Target based WSN faces coverage issue in which many targets could not be efficiently covered by static deployed sensors.
OBJECTIVES: This paper covers the issue of coverage problems by deploying the sensors to cover all the targets with minimized sensors in number.
METHODS: This paper proposes a Binary based Monkey King Evolutionary Algorithm for solving target based WSN problem, the proposed model consist a Binary method for converting the continuous values into binary form to solve the choice of potential position to place the sensors.
RESULTS: The proposed algorithm is evaluated in a 50x50 grid and 100x100 grid to track the performance and the performance of the proposed is compared with GA and PSO.
CONCLUSION: This paper utilized the MKE algorithm for improving the efficiency of the target coverage problem in WSN. It mainly focused on a single objective-based solution providing for small scale problems. From the simulation results, it is provided that the proposed MKE algorithm obtained 1.86 % of the F-value, which is higher than the other optimization algorithms such as GA and PSO.
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Copyright (c) 2019 EAI Endorsed Transactions on Internet of Things
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