LAUSR

Internet of Things (IoT) is enhancing the intelligence of the societies through a rapid transition to a smarter, automatic, responsive world due to the dramatic increase in the number of sensors deployed around the world. Collecting, modeling, and reasoning data generated by sensors play a crucial role in data analysis. Analyzing and interpreting real-time information transmitted through heterogeneous wireless networks are challenges that IoT applications encounter. Complex Event Processing (CEP) is a data stream tracking method used to extract the meaningful data obtained from the network results in real-time decision making. Instance data analysis, early diagnosis, and effective treatment of patients through the massive volume of data are considered indispensable parameters that have made the healthcare industry more reliant on real-time event processing than other industries. To achieve actionable insights, forecasting anomaly, and increasing healthcare quality, applying the CEP method is introduced in this area. In this paper, an event-driven IoT architecture is presented for data analysis of reliable healthcare applications, including context, event, and service layers. Dependability parameters are considered in each layer, and the CEP method as a novel solution and automated intelligence is applied in the event layer. Implementation results showed that the CEP method increased reliability, reduced costs, and improved healthcare quality.