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 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.