Error-Bounded Data Compression Using Data, Temporal and Spatial Correlations in Wireless Sensor Networks

Abstract

Due to the power is limited on each node of wireless sensor networks (WSNs), a data compression mechanism is required to achieve the purpose of power saving. In this paper, an efficient data compression method is proposed to reduce the size of transmission data under the given error bound. We first apply the observed transmission data to construct a static Huffman codebook which is related to the data correlation of the monitoring environment. Given an error bound, the proposed method determines whether the new sensed data should be sent or not by comparing it with the reference data such as the previous sensed data (for temporal correlation), the neighboring sensed data (for spatial correlation) and the codebook encoded data (for data correlation). Thus, the total size of transmission data can be minimized for power saving. Simulation results show that the proposed method can make WSNs more efficient in energy consumption. Even the error bound is set as a small value (under 0.009), the proposed method can reduce a lot of the transmission data (over 65%) to cut down the total energy consumption. Comparing to DF-TS, our improvement is nearly 70% in the total energy consumed