Distributed data aggregation in sensor networks by regression based compression

In this paper we propose a method for data compression and its subsequent regeneration using a polynomial regression technique. We approximate data received over the considered area by fitting it to a function and communicate this by passing only the coefficients that describe the function. In this paper, we extend our previous algorithm TREG to consider non-complete aggregation trees. The proposed algorithm DUMMYREG is run at each parent node and uses information present in the existing child to construct a complete binary tree. In addition to obtaining values in regions devoid of sensor nodes and reducing communication overhead, this new approach further reduces the error when the readings are regenerated at the sink. Results reveal that for a network density of 0.0025 and a complete binary tree of depth 4, the absolute error is 6%. For a non-complete binary tree, TREG returns an error of 18% while this is reduced to 12% when DUMMYREG is used

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