Location aware resource management in smart homes

The rapid advances in a wide range of wireless access technologies along with the efficient use of smart spaces have already set the stage for the development of smart homes. Context-awareness is perhaps the most salient feature in these intelligent computing platforms. The "location" information of the users plays a vital role in defining this context. To extract the best performance and efficacy of such smart computing environments, one needs a scalable, technology-independent location service. We have developed a predictive framework for location-aware resource optimization in smart homes. The underlying compression mechanism helps in efficient learning of an inhabitant\´s movement (location) profiles in the symbolic domain. The concept of Asymptotic Equipartition Property (AEP) in information theory helps to predict the inhabitant\´s future location as well as most likely path-segments with good accuracy. Successful prediction helps in pro-active resource management and on-demand operations of automated devices along the inhabitant\´s future paths and locations - thus providing the necessary comfort at a near-optimal cost. Simulation results on a typical smart home floor plans corroborate this high prediction success and demonstrate sufficient reduction in daily energy-consumption, manual operations and time spent by the inhabitant which are considered as a fair measure of his/her comfort.