IoT Data Management Architectures to Detect Critical Data Evolution

Internet of Things (IoT) interference in our lives is based on the large amount of sensor data, from complex and various sources (smart cities, industrial sensors, wearable devices, Health Care IoT), collected over time but also gathered every moment. All this data came in an amazing variety of formats and values. What we can do with all this data ? There are, mainly, two directions 1) act when values thresholds were exceeded ( heart rate, blood pressure, industrial critical values) or, 2) detect evolution patterns that can lead to dangerous situations (detect an increasing heart rate for a patient that can lead to critical situation in, say 5 minutes). Considering a large variety of sensors, up to past years, 2) was much more difficult to achieve but with greater impact (consider only the management of resources that can be directed to most critical situation). This paper proposes and discusses a system architecture using relational database (SQL) to store critical data, non-relational database (NoSQL) to store all data and IoT programmable gateways or a Critical Data Layer to send selected data to SQL database. Two strategies in detecting critical evolutions are discussed.