Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing
In this paper, we propose a bit depth compression (BDC) technique, which performs bit packing by dynamically determining the pack size based on the pattern of the bit depth level of the sensor data, thereby maximally reducing the space wastage that may occur during the bit packing process. The propo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8575 |
| _version_ | 1797572286459412480 |
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| author | Sang-Ho Hwang Kyung-Min Kim Sungho Kim Jong Wook Kwak |
| author_facet | Sang-Ho Hwang Kyung-Min Kim Sungho Kim Jong Wook Kwak |
| author_sort | Sang-Ho Hwang |
| collection | DOAJ |
| description | In this paper, we propose a bit depth compression (BDC) technique, which performs bit packing by dynamically determining the pack size based on the pattern of the bit depth level of the sensor data, thereby maximally reducing the space wastage that may occur during the bit packing process. The proposed technique can dynamically perform bit packing according to the data’s characteristics, which may have many outliers or several multidimensional variations, and therefore has a high compression ratio. Furthermore, the proposed method is a lossless compression technique, which is especially useful as training data in the field of artificial intelligence or in the predictive analysis of data science. The proposed method effectively addresses the spatial inefficiency caused by unpredictable outliers during time-series data compression. Additionally, it offers high compression efficiency, allowing for storage space savings and optimizing network bandwidth utilization while transmitting large volumes of data. In the experiment, the BDC method demonstrated an improvement in the compression ratio of up to 247%, with 30% on average, compared with other compression algorithms. In terms of energy consumption, the proposed BDC also improves data transmission using Bluetooth up to 34%, with 18% on average, compared with other compression algorithms. |
| first_indexed | 2024-03-10T20:54:05Z |
| format | Article |
| id | doaj.art-d153ea09eb86454b947add37c05aa633 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:05Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d153ea09eb86454b947add37c05aa6332023-11-19T18:05:01ZengMDPI AGSensors1424-82202023-10-012320857510.3390/s23208575Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit PackingSang-Ho Hwang0Kyung-Min Kim1Sungho Kim2Jong Wook Kwak3Gyeongbuk Institute of IT Convergence Industry Technology, Gyeongsan 38463, Republic of KoreaDepartment of Computer Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaGyeongbuk Institute of IT Convergence Industry Technology, Gyeongsan 38463, Republic of KoreaDepartment of Computer Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaIn this paper, we propose a bit depth compression (BDC) technique, which performs bit packing by dynamically determining the pack size based on the pattern of the bit depth level of the sensor data, thereby maximally reducing the space wastage that may occur during the bit packing process. The proposed technique can dynamically perform bit packing according to the data’s characteristics, which may have many outliers or several multidimensional variations, and therefore has a high compression ratio. Furthermore, the proposed method is a lossless compression technique, which is especially useful as training data in the field of artificial intelligence or in the predictive analysis of data science. The proposed method effectively addresses the spatial inefficiency caused by unpredictable outliers during time-series data compression. Additionally, it offers high compression efficiency, allowing for storage space savings and optimizing network bandwidth utilization while transmitting large volumes of data. In the experiment, the BDC method demonstrated an improvement in the compression ratio of up to 247%, with 30% on average, compared with other compression algorithms. In terms of energy consumption, the proposed BDC also improves data transmission using Bluetooth up to 34%, with 18% on average, compared with other compression algorithms.https://www.mdpi.com/1424-8220/23/20/8575sensor datalossless compressiontime series databit packingbit depth level |
| spellingShingle | Sang-Ho Hwang Kyung-Min Kim Sungho Kim Jong Wook Kwak Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing Sensors sensor data lossless compression time series data bit packing bit depth level |
| title | Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing |
| title_full | Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing |
| title_fullStr | Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing |
| title_full_unstemmed | Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing |
| title_short | Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing |
| title_sort | lossless data compression for time series sensor data based on dynamic bit packing |
| topic | sensor data lossless compression time series data bit packing bit depth level |
| url | https://www.mdpi.com/1424-8220/23/20/8575 |
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