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Sensors 2018, 18(1), 80;

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

1,2,* and 1,2
Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana 1000, Slovenia
Jožef Stefan International Postgraduate School, Ljubljana 1000, Slovenia
This paper is an extended version of our paper published in Janko, V.; Luštrek, M. Energy-efficient data collection for context recognition. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. Maui, HI, USA, 11–15 September 2017.
Author to whom correspondence should be addressed.
Received: 23 November 2017 / Revised: 22 December 2017 / Accepted: 26 December 2017 / Published: 29 December 2017
(This article belongs to the Special Issue Sensing, Data Analysis and Platforms for Ubiquitous Intelligence)
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The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy. View Full-Text
Keywords: context recognition; optimization; modeling; energy efficiency; Markov chains context recognition; optimization; modeling; energy efficiency; Markov chains

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Janko, V.; Luštrek, M. Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition. Sensors 2018, 18, 80.

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