Complex Event driven data analysis approach in fog architecture based Smart healthcare

Managing and processing the huge amount of heterogeneous data generated from IoT is the most difficult task. It called into question the computational storage capacity and efficiency of today's infrastructure resources. Moreover, it becomes difficult to provide the user with significant information to make a decision due to the existence of the massive data. Complex Event Processing (CEP) technology has developed as an effective method for extracting critical insights from enormous streams of data in real time. Here an attempt is made to design a system that can generate complex events from analysis of large sensor data and send only critical events to the cloud server. We have included data analytic engine on the e, dge device which will take advantage of CEP technology and considerable reduction in data volume is carried out as we are pushing only Complex events. Our research mainly focuses on the design of this computationally efficient analytic engine for the data reduction in Smart healthcare applications. It avoids sending unnecessary event data on the cloud; subsequently, reduce the data transfer time and memory space. We have developed hierarchical event model on the edges in smart health care application and used the fuzzy based approach for analytical processor design on the edges. Application mainly focuses on the online ECG monitoring for the critical patients.