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An Evolutionary Computing Based Approach for Optimal Target Coverage in Wireless Sensor Networks

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Part of the Smart Innovation, Systems and Technologies book series (SIST,volume 310)

Abstract

Wireless Sensor Networks (WSNs) are widely used for surveillance and monitoring tasks. Coverage control of wireless sensor networks deals with optimization of sensor deployments to satisfy k–coverage of targets. In this paper, a mathematical model of coverage control while optimizing the overall cost is presented. A Genetic Algorithm (GA) is used to optimize the coverage control problem to minimize the cost while satisfying k–coverage constraint. Various initial sensor deployment models are tested and compared. Both static and dynamic hyperparameter tuning methods such as grid search, Dynamic Increasing of Low Mutation ratio/Dynamic Decreasing of High Crossover ratio (ILM/DHC), and Dynamic Decreasing of High Mutation ratio/Dynamic Increasing of Low Crossover ratio (DHM/ILC) are tested. The evolutionary computing based solution is able to optimize the placement of sensors for various coverage scenarios.

Keywords

  • Wireless sensor network
  • Genetic algorithm
  • Coverage control
  • Cost optimization
  • Deployment models
  • k–coverage

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  • DOI: 10.1007/978-981-19-3455-1_5
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Correspondence to Sheikh Nooruddin .

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Nooruddin, S., Islam, M.M., Karray, F. (2022). An Evolutionary Computing Based Approach for Optimal Target Coverage in Wireless Sensor Networks. In: Zimmermann, A., Howlett, R.J., Jain, L.C. (eds) Human Centred Intelligent Systems. Smart Innovation, Systems and Technologies, vol 310. Springer, Singapore. https://doi.org/10.1007/978-981-19-3455-1_5

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