Abstract
Bio-inspired metaheuristics can be useful for the optimization of complex systems. Wireless sensor networks (WSNs) are massively distributed cyber-physical systems whose efficient operation requires appropriate design and control strategies. In certain contexts, like with randomly deployed WSNs, the physical network configuration can be affected only minimally, and optimal control strategies are crucial for optimizing network performance metrics like lifetime, coverage, and energy consumption. These metrics often conflict with each other, making network optimization a complex multi-objective problem. In this study, we introduce an improved version of a bi-objective genetic algorithm for the optimization of sensor network lifetime and target coverage. The new algorithm uses the generic evolutionary optimization framework together with a problem-specific heuristic mutation operator. We investigate the ability of the algorithm to find sensor schedules that extend network lifetime, and improve average target coverage while satisfying the minimum coverage requirement and show that the improved algorithm delivers better schedules than the original GA.
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Acknowledgements
The authors would like to thank the following computing centres where the computation of the project was performed: Academic Computer Center in Gdańsk (TASK), and Wroclaw Centre for Networking and Supercomputing (WCSS). This work was also supported by the Czech Science Foundation in the project “Constrained multi-objective Optimization Based on Problem Landscape Analysis” funded by the Czech Science Foundation (grant no. GF22-34873K) and in part by the grant of the Student Grant System no. SP2023/12, VSB - Technical University of Ostrava.
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Dua, A., Krömer, P., Czech, Z.J., Jastrząb, T. (2023). Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network Optimization. In: Barolli, L. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-031-40971-4_17
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