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Adaptive sampling in context-aware systems: a machine learning approach

Adaptive sampling in context-aware systems: a machine learning approach
Adaptive sampling in context-aware systems: a machine learning approach
As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification.
Wood, Alex L.
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Merrett, Geoff V.
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Gunn, Steve R.
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Al-Hashimi, Bashir M.
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Shadbolt, Nigel R
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Hall, Wendy
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Wood, Alex L.
0e658cee-1b98-45d7-b77f-b91470c764d8
Merrett, Geoff V.
89b3a696-41de-44c3-89aa-b0aa29f54020
Gunn, Steve R.
306af9b3-a7fa-4381-baf9-5d6a6ec89868
Al-Hashimi, Bashir M.
0b29c671-a6d2-459c-af68-c4614dce3b5d
Shadbolt, Nigel R
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c

Wood, Alex L., Merrett, Geoff V., Gunn, Steve R., Al-Hashimi, Bashir M., Shadbolt, Nigel R and Hall, Wendy (2012) Adaptive sampling in context-aware systems: a machine learning approach. IET Wireless Sensor Systems 2012, London, United Kingdom. 18 - 19 Jun 2012. 5 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

As computing systems become ever more pervasive, there is an increasing need for them to understand and adapt to the state of the environment around them: that is, their context. This understanding comes with considerable reliance on a range of sensors. However, portable devices are also very constrained in terms of power, and hence the amount of sensing must be minimised. In this paper, we present a machine learning architecture for context awareness which is designed to balance the sampling rates (and hence energy consumption) of individual sensors with the significance of the input from that sensor. This significance is based on predictions of the likely next context. The architecture is implemented using a selected range of user contexts from a collected data set. Simulation results show reliable context identification results. The proposed architecture is shown to significantly reduce the energy requirements of the sensors with minimal loss of accuracy in context identification.

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More information

e-pub ahead of print date: 2012
Published date: 25 May 2012
Venue - Dates: IET Wireless Sensor Systems 2012, London, United Kingdom, 2012-06-18 - 2012-06-19
Organisations: Web & Internet Science, Electronic & Software Systems

Identifiers

Local EPrints ID: 339172
URI: http://eprints.soton.ac.uk/id/eprint/339172
PURE UUID: 4b5a1c39-b6df-490f-a909-c67321d4940a
ORCID for Geoff V. Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811

Catalogue record

Date deposited: 25 May 2012 15:25
Last modified: 15 Mar 2024 03:23

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Contributors

Author: Alex L. Wood
Author: Geoff V. Merrett ORCID iD
Author: Steve R. Gunn
Author: Bashir M. Al-Hashimi
Author: Nigel R Shadbolt
Author: Wendy Hall ORCID iD

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