A sensor array produces lots of bits of data every sample period, which may cause a heavy burden on the long-distance wireless data transmission, especially in the scenarios of wireless sensor networks. A lossy but error-bounded sensor array data compression algorithm is proposed, in which the major part of the sensor array data are approximated by the Catmull-Rom spline curve and the residual errors are quantized and encoded with Huffman coding. The performance of this algorithm has been evaluated with a real data set from the wireless hydrologic monitoring system deployed in Qinhuangdao Port of China. The results show that the algorithm performs well for the hydrologic sensor array data.
Results of hydrologic monitoring on landslide-prone coastal bluffs near Mukilteo, Washington