Multisignal 1-D compression by F-transform for wireless sensor networks applications |
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| Affiliation: | 1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy;1. Département génie électrique, Ecole Mohamamdia d’Ingénieurs (EMI), Université Mohammed V Agdal, Rabat, Morocco;2. Laboratoire de Recherche en Economie de l’Energie, Environnement et Ressources, Département d’Economie, University Caddy Ayyad, Marrakech, Morocco;1. Virtual Systems Research Centre, University of Skövde, Skövde 54128, Sweden;2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA;3. Amazon Development Centre (India) Pvt. Ltd., Bengaluru 560055, India;4. Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra 416415, India;5. General Motors R&D Center, Warren, MI 48090, USA;1. College of Mathematics, Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China;2. College of Engineering, Shaoxing University, Shaoxing 312000, China;1. College of Finance, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China;2. School of Economics and Management, Southeast University, Nanjing 210096, Jiangsu, China |
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| Abstract: | 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. |
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| Keywords: | Data compression Wireless sensor networks F-transform Least-squares |
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