ArticleSensors (Basel, Switzerland)2017
Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition.Sensors (Basel, Switzerland) · 2021Article
- A General Framework for Making Context-Recognition Systems More Energy Efficient.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
Funding
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Abstract
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.
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Registered trials
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