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Wireless Sensors enable fine grain monitoring of activities of individual and social interest. Typically these sensors sense & send data continuously directly or through other sensor nodes to a base station. Wireless Sensor Data are inherently noisy and have frequent random spikes due to dynamic nature of the medium. Hence, the decision at the receiving node based on such data is likely to be erroneous. Erroneous data and decisions may affect its transformation to meaningful form like 'Context'. It is therefore desirable to clean the data for improved context extraction. Bayesian Belief Networks are used here to quantitatively encode the dependencies among various sensors. These dependencies are then used to estimate missing data and also to detect and recover from errors.
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