Data Reduction for real-time bridge vibration data on Edge

Abstract

In the Internet of Things (IoT) era, with the growing number of data sources, we need to face some challenges such as high cost of the cloud storage caused by large amounts of data. To minimize the communication time and enhance the performance, sending the entire large amount of data is not practical. Thus, it is appropriate to make use of edge computing, or data preprocessing on IoT gateways. In this paper, we propose a data reduction algorithm for the gateway of bridge vibration G-sensors. The data reduction algorithm is based on a pattern system, which is comprised of a pattern library and a pattern classifier. The pattern library is generated by using the K-means clustering method. The results show that the proposed approach is effective in data reduction and outlier detection for bridge vibration data collection on the IoT gateway.