Tree based data aggregation in sensor networks using polynomial regression

In this paper, we propose a tree based regression algorithm, (TREG) that addresses the problem of data compression in wireless sensor networks. By function approximation based on multivariable polynomial regression and passing only the coefficients returned by the regression function instead of aggregated data, TREG achieves the following goals: (1) the sink can get attribute values in regions devoid of sensor nodes for attribute values that show smooth spatial gradation (2) readings over any portion of the region can be obtained at one time by querying the root instead of flooding those regions, thus incurring significant energy savings. As size of the data packet transmitted, from one tree node to another remains constant, the proposed scheme scales well with growing network density. Extensive simulations are performed on real world data to demonstrate the effectiveness of our aggregation algorithm. Results reveal that for a network density of 0.0025, the optimal tree-depth should be 4 in order to restrict the absolute error to less than a threshold of 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of tree depth.