Wood, Alex L., Merrett, Geoff V., Gunn, Steve R., Al-Hashimi, Bashir M., Shadbolt, Nigel R and Hall, Wendy
Adaptive sampling in context-aware systems: a machine learning approach
At IET Wireless Sensor Systems 2012, United Kingdom.
18 - 19 Jun 2012.
- 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.
Conference or Workshop Item
|Venue - Dates:
||IET Wireless Sensor Systems 2012, United Kingdom, 2012-06-18 - 2012-06-19
||Web & Internet Science, Electronic & Software Systems
|2012||e-pub ahead of print|
|25 May 2012||Published|
||25 May 2012 15:25
||17 Apr 2017 17:05
|Further Information:||Google Scholar|
Actions (login required)