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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.