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In wireless sensor networks, since each of the sensors has limited power, the problem of transmission cost is a critical issue. A useful solution is to take the advantage of distributed signal processing techniques that uses data decorrelation among sensors and results in reduction of data gathering and transmission cost. We propose a distributed wavelet algorithm, based on the Lifting scheme, as a means to decorrelate data. This is our adaptive algorithm which chooses the number of decomposition levels, based on the network structure, power constraint in sensor nodes and data correlation among sensors, so that results considerable reduction in transmission costs and approximation error. Finally, numerical simulations show substantial improvements for data gathering in terms of total communication costs and reconstruction error for proposed method.