Multisignal 1-D compression by F-transform for wireless sensor networks applications

Graphical abstractExample 1: MSE behaviour for ambient temperature (dotted, blocks with CR=1.33; dashed, LS with CR=1.33; dot-dashed, blocks with CR=1.83; continuous, LS with CR=1.83; thick, DWT). Display Omitted HighlightsIn WSNs data compression is a way for transferring a large amount of data to a sink.When data are not correlated popular methods such as DWT do not perform well.We propose two F-transform based techniques to address these issues.Publicly available environmental data were used for a comparative study.If compared with DWT our approaches allow higher data compression rates with lower distortions. In wireless sensor networks a large amount of data is collected for each node. The challenge of transferring these data to a sink, because of energy constraints, requires suitable techniques such as data compression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popular in this field. These methods behave well enough if there is a correlation in data. However, especially for environmental measurements, data may not be correlated. In this work, we propose two approaches based on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Discrete Wavelet Transform on publicly available real-world data sets. The comparative study shows the capabilities of our approaches, which allow a higher data compression rate with a lower distortion, even if data are not correlated.