Robust Data Compression for Irregular Wireless Sensor Networks Using Logical Mapping

We propose and evaluate RIDA, a novel robust information-driven data compression architecture for distributed wireless sensor networks, allowing it to conserve energy and bandwidth. The key idea is to determine the data correlation among a group of sensors based on the values of the data itself to significantly improve compression performance. Hence, this approach moves beyond traditional data compression schemes that rely on spatial data correlation. A logical mapping approach assigns virtual indices to nodes based on the data content, which enables simple implementation of data transformation on resourceconstrained nodes without any other information. The logical mapping approach also adapts particularly well to irregular sensor network topologies. We evaluate our architecture with both Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on publicly available real-world data sets. Our experiments on both simulation and real data show that 30% of energy and 80-95% of the bandwidth can be saved for typical multi-hop data networks. Moreover, the original data can be retrieved after decompression with a low error of about 3%. In particular, for one state-of-the-art distributed data compression algorithm for sensor networks [1], we show that the compression ratio is doubled by using logical mapping while maintaining comparable mean square error. Furthermore, we also propose a mechanism to detect and classify missing or faulty nodes, showing accuracy and recall of 95% when half of the nodes in the network are missing or faulty.