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A large number of context-inference applications run on off-the-shelf smart-phones and infer context from the data acquired by means of the sensors embedded in these devices. The use of efficient and effective sampling technique is of key importance for these applications. Aggressive sampling can ensure a more fine-grained and accurate reconstruction of context information but, at the same time, continuous querying of sensor data might lead to rapid battery depletion. In this paper, the authors present an adaptive sensor sampling methodology which relies on dynamic selection of sampling functions depending on history of context events.
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