Correlation analysis and applications in wireless microsensor networks

Sensor readings in a wireless microsensor network are correlated both spatially and temporally. Various coding and storage schemes and also other applications have been developed to exploit these correlations; therefore it is crucial to efficiently track the correlations. In this paper, a linear prediction algorithm is developed to initially establish the correlations, and the order of linear prediction has been derived from the prediction error power distribution. A tracking algorithm uses discrete Kalman filter to track the correlation once it is initially obtained. This Kalman filter based algorithm uses the gradient computed at each step as the input control vector. This approach is suitable for quantifying geographical spatial correlation and multimodality correlation. Experimental results using various data sets have shown that the proposed scheme can accurately obtain the correlation and consumes much less energy as compared to known schemes.