Data Reduction for realtime bridge vibration data on Edge 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 Gsensors. 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 Kmeans 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.

Dynamic clustering method based on power demand and information volume for intelligent and green IoT Abstract   The Internet of Things (IoT) is an integrated part of the future network which requires comprehensive awareness, reliable delivery and intelligent processing. Therefore, it faces the challenge of energy efficiency during realtime implementations based on respective applications. The work proposes an efficient and green dynamic clustering mechanism based on power demand and information volume. The machine learning approach is based on dynamic cluster formation by avoiding any information loss. Initially, among all the available Poisson distributed nodes, the master node divides them into two temporary clusters based on their power requirements. Then, the power requirements within these two clusters are equalized by a set threshold and based on application requirements, the dynamic clusters are formed by measuring the amount of information in each cluster. The simulation results presents that the proposed method equalizes the power demand of the network and maximizes the information in the cluster, which further improves the energy efficiency of the whole network. The results are also compared with traditional methods to justify the proposed work.