FuzzyCAT: A lightweight Adaptive Transform for sensor data compression
Vasilisa Bashlovkina, Mohamed Abdelaal, Oliver Theel · 2015
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.