Abstract
The use of sensor applications has been steadily increasing, leading to an urgent need for efficient data compression techniques to facilitate the storage, transmission, and processing of digital signals generated by sensors. Unlike other sequential data such as text sequences, sensor signals have more complex statistical characteristics. Specifically, in every signal point, each bit, which corresponds to a specific precision scale, follows its own conditional distribution depending on its history and even other bits. Therefore, applying existing general-purpose data compressors usually leads to a relatively low compression ratio, since these compressors do not fully exploit such internal features. What is worse, partitioning a bit stream into groups with a preset size will sometimes break the integrity of each signal point. In this paper, we present a lossless data compressor dedicated to compressing sensor signals which is built upon a novel recurrent neural architecture named multi-channel recurrent unit (MCRU). Each channel in the proposed MCRU models a specific precision range of each signal point without breaking data integrity. During compressing and decompressing, the mirrored network will be trained on observed data; thus, no pre-training is needed. The superiority of our approach over other compressors is demonstrated experimentally on various types of sensor signals.
Highlights
Accepted: 29 October 2021As digitalization advances continuously, sensor technology is undergoing tremendous development and has been widely used in applications such as wearable medical devices [1], climate change tracking [2], and infrastructure monitoring [3]
We present a novel recurrent neural network (RNN) architecture that is specially designed for modeling sensor signals as the probability predictor of a contextbased lossless compressor
In this sub-section, we present a novel multi-channel recurrent unit (MCRU), which is specially designed as a deep-learning predictor for sensor signals
Summary
Sensor technology is undergoing tremendous development and has been widely used in applications such as wearable medical devices [1], climate change tracking [2], and infrastructure monitoring [3]. The compressors mentioned above are general-purpose methods which perform data compression by reducing the redundant information between data points. For sensor signals, this inter-point relationship may be the continuity and periodicity. Dai et al proposed a lossless compression method for periodic signals based on an adaptive dictionary model which can predict the current data value according to the history [19]. Huang et al proposed a novel ECG signal prediction model that uses an autoregressive integrated moving average (ARIMA) model and discrete wavelet transform (DWT) [20] These compressors are all designed for specific signals. The effectiveness of the proposed approach is demonstrated experimentally on different types of sensor signals
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