Exploiting user behaviour for context-aware power management

With more and more computing devices being deployed in buildings there has been a steady rise in buildings´ electricity consumption. At the same time there is a pressing need to reduce overall building energy consumption. Pervasive computing could further exacerbate this problem but it could also provide a solution. Context information (e.g., user location) likely to be available in pervasive computing environments could enable highly effective device power management. The objective of such context-aware power management (CAPM) is to minimise the overall electricity consumption of a building while maintaining acceptable user-perceived device performance. To investigate the potential of CAPM we conducted experimental trials for two simple location-aware power management policies. Our results highlight the presence of two distinct user behaviour patterns but also show that location alone is not enough for effective power management. We therefore propose a CAPM framework that employs Bayesian networks to support prediction of user behaviour patterns from multi-modal sensor data for effective power management. We further propose the use of acoustic data as an interesting context for predicting finer-grained user behaviour. The paper presents an initial evaluation of the resulting framework.