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Mobile device based human-centric sensing and user state recognition provide rich contextual information for various mobile applications and services. However, continuously capturing this contextual information consumes significant amount of energy and drains mobile device battery quickly. In this paper, the authors propose a computationally efficient algorithm to obtain the optimal sensor sampling policy under the assumption that the user state transition is Markovian. This Markov-optimal policy minimizes user state estimation error while satisfying a given energy consumption budget.
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