The Challenge of Continuous Mobile Context Sensing
In this paper, we highlight the challenge of continuously sensing context data from mobile phones. In particular, we show that the energy cost of this type of continuous sensing is extremely high if a) accuracy is desired, and b) power optimisations do not work well if multiple tasks are sensing con...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2014
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/2062 https://ink.library.smu.edu.sg/context/sis_research/article/3061/viewcontent/comsnets14_sensing_av.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | In this paper, we highlight the challenge of continuously sensing context data from mobile phones. In particular, we show that the energy cost of this type of continuous sensing is extremely high if a) accuracy is desired, and b) power optimisations do not work well if multiple tasks are sensing concurrently. Our results are derived from our experience in building the LiveLabs context sensing platform. We present results for different types of sensing tasks; ranging from simple sensing using just one sensor all the way to multi-sensor sensing performed by concurrent high-level tasks. We end with a discussion of the challenges of supporting multi-task sensing across heterogeneous devices and operating systems. |
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