Three Methods for Energy-Efficient Context Recognition

Authors

  • Vito Janko Jožef Stefan Institute

DOI:

https://doi.org/10.31449/inf.v45i2.3509

Abstract

Context recognition is a process where (usually wearable) sensors are used to determine the context (location, activity, etc.) of users wearing them. A major problem of such context-recognition systems is the high energy cost of collecting and processing sensor data. This paper summarizes a doctoral thesis that focuses on solving this problem by proposing a general methodology for increasing the energy-efficiency of context-recognition systems. The thesis proposes and combines three different methods that can adapt a system’s sensing settings based on the last recognized context and last seen sensor readings.

References

Wang, Yi, et al. "A framework of energy efficient mobile sensing for automatic user state recognition." Proceedings of the 7th international conference on Mobile systems, applications, and services. 2009.

Khan, Aftab, et al. "Optimising sampling rates for accelerometer-based human activity recognition." Pattern Recognition Letters 73 (2016): 33-40.

Janko, Vito. Adapting sensor settings for energy-efficient context recognition. Diss. Ph. D. thesis, Jožef Stefan International Postgraduate School, 2020.

Lomax, Susan, and Sunil Vadera. "A survey of cost-sensitive decision tree induction algorithms." ACM Computing Surveys (CSUR) 45.2 (2013): 1-35.

Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6.2 (2002): 182-197

EECR, https://pypi.org/project/eecr/, Last accessed: 08-03-2021

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Published

2021-06-15

How to Cite

Janko, V. (2021). Three Methods for Energy-Efficient Context Recognition. Informatica, 45(2). https://doi.org/10.31449/inf.v45i2.3509

Issue

Vol. 45 No. 2 (2021)

Section

Thesis summary

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