FuzzyCAT: A lightweight Adaptive Transform for sensor data compression

This paper aims at developing a novel high precision compression method. Based on the previously developed Fuzzy Transform Compression (FTC), we design and implement a modified version of the algorithm, referred to as Fuzzy Compression Adaptive Transform (FuzzyCAT). The underlying idea of FuzzyCAT is to adapt the transform parameters to the signal´s curvature inferred from the time derivatives. FuzzyCAT outperforms the original FTC while preserving its favorable qualities like periodicity and resilience to lost packets. It also shows a competitive edge over the Lightweight Temporal Compression (LTC) method. A series of experiments with a network of TelosB sensor nodes revealed that transmission costs of the FuzzyCAT algorithm is much less than that of LTC at the expense of a slight increase in processing power. This makes it an outstanding candidate for data compression in wireless sensor networks.