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Robust IoT time series classification with data compression and deep learning
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DOI：10.1016/j.neucom.2020.02.097.png)
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Abstract
En 中文
Internet of Things (IoT) and wearable systems are very resource limited in terms of power, memory, bandwidth and processor performance. Sensor time series compression can be regarded as a direct way to use memory and bandwidth resources efficiently. On the other hand, the time series classification has recently attracted great attention and has found numerous potential uses in areas such as finance, industry and healthcare. This paper investigates the effect of lossy compression techniques on the time series classification task using deep neural networks. Furthermore, this paper proposes an efficient compression approach for univariate and multivariate time series that combines the lifting implementation of the discrete wavelet transform with an error-bound compressor, namely Squeeze (SZ), to attain an optimal trade-off between data compression and data quality. (C) 2020 Published by Elsevier B.V.
Keywords:
IoT applications
Energy efficiency
Lossy compression
Data reduction
Time series classification
Deep neural networks
Discrete wavelet transform
lifting scheme
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