A Proactive Data Service Model to Encapsulating Stream Sensor Data into Service.

Abnormality Detection in power plant is a typical IoT application which aims to identify anomalies in these routinely collected monitoring sensor data; intend to help detect possible faults in the equipment. However, on the development of abnormality detection, we find that there are three challenges. The first one is the lack of cooperation between sensors. It means that the physical sensors cannot share and interact with each other. Secondly, the rapid increase in volume of sensor data and dynamic situation of production result in challenges to predefine all possible associations between sensors. Thirdly, it is difficult to build IoT application for developers who have little or no professional knowledge about production process. In this paper, we proposed a proactive data service model to encapsulate stream sensor data into services. We spread events among the proactive data services. By analysis of event correlations, we have realized service hyperlinks which help to offer the proactive real-time interaction with services. Real application and experiments verified that our proactive data service based method is more effective compare with traditional rule-based methods to detect abnormalities in power plant.