Fog Intelligence for Real-Time IoT Sensor Data Analytics

• Main Authors: Hazem M. Raafat, M. Shamim Hossain, Ehab Essa, Samir Elmougy, Ahmed S. Tolba, Ghulam Muhammad, Ahmed Ghoneim
• Format: Article
• Language: English
• Published: IEEE 2017-01-01
• Series: IEEE Access
• Subjects: Fog computing; novelty detection; sensor signals; Internet of Things (IoT); Levenes test; statistical features
• Online Access: https://ieeexplore.ieee.org/document/8047248/

The evolution of the Internet of things and the continuing increase in the number of sensors connected to the Internet impose big challenges regarding the management of the resulting deluge of data and network latency. Uploading sensor data over the web does not add value. Therefore, an efficient knowledge extraction technique is badly needed to reduce the amount of data transfer and to help simplify the process of knowledge management. Homoscedasticity and statistical features extraction are introduced in this paper as novelty detection enabling techniques, which help extract the important events in sensor data in real time when used with neural classifiers. Experiments have been conducted on a fog computing platform. System performance has been also evaluated on an occupancy data set and showed promising results.