Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction

Jeng-Wei Lin, Shih-wei Liao and Fang-Yie Leu
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Jeng-Wei Lin: Department of Information Management, Tunghai University, Taichung 40704, Taiwan
Shih-wei Liao: Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan
Fang-Yie Leu: Department of Computer Science, Tunghai University, Taichung 40704, Taiwan

Energies, 2019, vol. 12, issue 13, 1-20

Abstract: Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experimental results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power.

Keywords: Internet of Things; big data; data compression; bounded-error approximation; piecewise linear; resolution reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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