An Evolutionary Computing Based Approach for Optimal Target Coverage in Wireless Sensor Networks
Smart Innovation, Systems and Technologies
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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Cost optimization, Coverage control, Deployment models, Genetic algorithm, k–coverage, Wireless sensor network, Constraint satisfaction problems, Wireless sensor networks, Costs Optimization, Coverage control, Deployment models, Evolutionary computing, K-coverage, Monitoring tasks, Optimal target, Sensors deployments, Surveillance task, Target coverage
S. Nooruddin, M.M. Islam, and F. Karray, "An Evolutionary Computing Based Approach for Optimal Target Coverage in Wireless Sensor Networks", in Intl. KES Conference on Human Centred Intelligent Systems, (KES HCIS 2022), in Smart Innovation, Systems and Technologies, vol. 310, pp. 53-69, Jun 2022, doi:10.1007/978-981-19-3455-1_5