Interleaving Wavelet Coefficients for Adaptive Data Transmission from Pervasive Sensing Systems

The paper describes a method for adaptive transmission of data over error-prone wireless links and suited to the limitations of embedded pervasive sensing systems. The technique performs a multi-resolution wavelet transform on the data followed by interleaving the resulting coefficients among the transmitted packets. The interleaving scheme is designed to facilitate estimation of coefficients lost during transmission by minimizing the correlation between elements in one packet. A correlation model between detail coefficients obtained from a wavelet transform of Gauss-Markov processes is used to estimate the optimal distribution of coefficients to packets. The interleaving ensures that packet loss results in non-contiguous "gaps" in the reconstructed wavelet tree. The original data is reconstructed by polynomial interpolation of the missing coefficients from correlated neighboring coefficient. We present simulation results that show the performance of our scheme on both real-world and simulated datasets.