A Novel Fog Computing Enabled Temporal Data Reduction Scheme in IoT Systems

Abstract

The recent advancement of Internet of Things (IoT) technologies has enabled many emerging applications, including smart building and connected vehicles. These advanced applications generate massive amount of data at the edge of IoT networks, which usually need to be relayed to a remote data center for further real-time processing. However, uploading all these IoT data to the cloud platform imposes a heavy burden on the underlying network. The unavoidable long delay from data exchange and processing significantly reduces the time-responsiveness of real-time IoT applications. Recently, fog computing has been introduced to IoT applications as an intermediate between end devices and cloud for primary IoT data processing. In this paper, a temporal IoT data reduction scheme through fog computing is proposed to reduce the total amount of IoT data uploaded to the cloud. More specifically, IoT data are first modeled as multivariate normal distribution by the cloud. Dual Kalman filters (KF) with identical parameters are then deployed at both the cloud and fog platforms. The same predictions are simultaneously triggered by the dual KFs at both platforms. Only the measured IoT data out of predicted range are further uploaded from fog to cloud. Otherwise, predicted values at both platforms are used instead of measurements. A simple prototype IoT system is developed for performance evaluation. Experimental results indicate that the proposed scheme significantly reduces the number of packets uploaded to the cloud platform with high data accuracy.