Major Coefficients Recovery: A Compressed Data Gathering Scheme for Wireless Sensor Network

For large-scale sensor networks deployed for data gathering, energy efficiency is critical. Eliminating the data correlation is a promising technique for energy efficiency. Compressive Data Gathering (CDG) [8], which employs distributed coding to compress data correlation, is an important approach in this area. However, the CDG scheme uses a uniform pattern in data transmission, where all nodes transmit the same amount of data regardless of their hop distances to the sink, making it inefficient in saving transmission costs in 2-D networks. In this paper, the Major Coefficient Recovery (MCR) scheme is proposed, where the Discrete Cosine Transformation (DCT) is applied in a distributed fashion to the original sensed data. A non-uniform data transmission pattern is proposed by exploiting the energy concentration property of DCT and QR decomposition techniques so that sensors with larger hop-count can transmit fewer messages for network energy efficiency. The sink node recovers only the major coefficients of the DCT to reconstruct the original data accurately. MCR reduces the transmission overhead to O(kn - k²), an improvement by O(logn) over CDG in both 1-D and 2-D cases. The recovery performance of MCR is verified by extensive simulations.