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

Abstract

For large-scale sensor networks deployed for data gathering, energy efficiency is critically required. Elimination the data correlation is a promising technique for energy efficiency. Compressive Data Gathering (CDG) 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 not efficient in saving transmission cost in the 2-D networks. In this paper, Major Coefficient Recovery (MCR) scheme is proposed, where Discrete Cosine Transformation (DCT) is applied distributively to the original data sensed. 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 less messages for network energy efficiency. The sink node recovers only the major coefficients of the DCT to recover the original data accurately. MCR reduces the transmission overhead to $O(kn-k^2)$, which is $O(\log{n})$ better than CDG in both 1-D and 2-D cases. The recovery performances of MCR are verified by extensive simulations.