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Many pervasive computing applications continuously monitor state changes in the environment by acquiring, interpreting and responding to information from sensors embedded in the environment. However, it is extremely difficult and expensive to obtain a continuous, complete, and consistent picture of a continuously evolving operating environment. One standard technique to mitigate this problem is to employ mathematical models that compute missing data from sampled observations thereby approximating a continuous and complete stream of information. However, existing models have traditionally not incorporated a notion of temporal validity, or the quantification of imprecision associated with inferring data values from past or future observations.
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