Adaptive sampling in context-aware systems: a machine learning approach
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. In, IET Wireless Sensor Systems 2012, London, GB, 18 - 19 Jun 2012. 5pp.
- Accepted Manuscript
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Electronic & Software Systems
Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Web & Internet Science
|Date Deposited:||25 May 2012 15:25|
|Last Modified:||27 Mar 2014 20:22|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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