Abstract
Context recognition (CR) systems infer the user’s context, such as their physical activity, from sensor data obtained, for example, with smartphone sensors. Designing an energy-efficient CR system, however, is a complex optimization problem involving conflicting objectives and several constraints arising from real-world limitations and designers’ preferences. To address this task, we propose a constrained multiobjective formulation of the CR design problem. Unlike most studies in this domain, we use a true multiobjective approach in solving it. Specifically, we apply a multiobjective evolutionary algorithm equipped with two different constraint handling techniques. Their performance is demonstrated in optimizing six CR systems of various complexity. The proposed problem formulation and the optimization results make it possible to better understand the CR systems operation and provide valuable information to the designers.
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Acknowledgment
This work is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under Grant Agreement no. 692286. We also acknowledge financial support from the Slovenian Research Agency (young researcher program and research core funding no. P2-0209).
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Vodopija, A., Janko, V., Luštrek, M., Filipič, B. (2020). Constrained Multiobjective Optimization for the Design of Energy-Efficient Context Recognition Systems. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_24
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DOI: https://doi.org/10.1007/978-3-030-63710-1_24
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