Abstract
When designing a wireless sensor network several performance metrics should be considered, e.g., network lifetime, target coverage, sensor energy consumption. As a rule, these metrics are in conflict with each other, which means that by optimizing some of them we worsen the others. Designing the network is therefore a problem of multi-objective optimization. In this work, we propose a bi-objective genetic algorithm that optimizes network lifetime and target coverage. We consider two variants of the algorithm, in which the fitness function comprises only the network lifetime, or where it includes both, the network lifetime and target coverage. This makes it possible to find a trade-off between these two objectives. In-depth experimental studies are carried out for both variants of the algorithm.
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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 Ministry of Education, Youth and Sports of the Czech Republic in the project “Metaheuristics Framework for Multi-objective Combinatorial Optimization Problems (META MO-COP)”, reg. no. LTAIN19176, and in part by the SGS grants no. SP2022/11 and SP2022/77, VSB-TU Ostrava. Czech Republic.
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Dua, A., Krömer, P., Czech, Z.J., Jastrząb, T. (2022). A Bi-objective Genetic Algorithm for Wireless Sensor Network Optimization. In: Barolli, L. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2022. Lecture Notes in Networks and Systems, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-08812-4_15
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DOI: https://doi.org/10.1007/978-3-031-08812-4_15
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