On-line Interpolation-based Data Management for Time-Series Data toward Edge Computing

In recent years, a vast amount of sensor information has been generated as a result of the widespread use of IoT devices. However, with the explosion of data volume, it has become difficult to process all the data centrally on cloud servers. Therefore, it has become important to process data at edge devices where it is acquired. As edge devices cannot store all the acquired data, it is necessary to select and store the important data from the acquired data. In this paper, we propose an online data management method for time-series data called O-IDM, which is designed to extract data to preserve characteristics. O-IDM can extract data necessary to approximate the original data with a small computational cost. We evaluated the extraction performance of the proposed method by comparing it with other data extraction methods using the degree of deviation between the input data and polygonal line approximation base on the extracted data. The experimental results show that the proposed method was able to extract data with high accuracy. Further, the proposed approach was able to reduce the amount of data transmission required to convey the characteristics of the data by up to 66.7 percentage point compared to the naïve time-based sampling method.