Multi-Layer Cross Domain Reasoning over Distributed Autonomous IoT Applications

Due to the rapid advancements in the sensor technologies and IoT, we are witnessing a rapid growth in the use of sensors and relevant IoT applications. A very large number of sensors and IoT devices are in place in our surroundings which keep sensing dynamic contextual information. A true potential of the wide-spread of IoT devices can only be realized by designing and deploying a large number of smart IoT applications which can provide insights on the data collected from IoT devices and support decision making by converting raw sensor data into actionable knowledge. However, the process of getting value from sensor data streams and converting these raw sensor values into actionable knowledge requires extensive efforts from IoT application developers and domain experts. In this paper, our main aim is to propose a multi-layer cross domain reasoning framework, which can support application developers, end-users and domain experts to automatically understand relevant events and extract actionable knowledge with minimal efforts. Our framework reduces the efforts required for IoT applications development (i) by supporting automated application code generation and access mechanisms using IoTSuite, (ii) by leveraging from Machine-to-Machine Measurement (M3) framework to exploit semantic technologies and domain knowledge, and (iii) by using automated sensor discovery and complex event processing of relevant events (ACEIS Middleware) at the multiple data processing layers and different stages of the IoT application development life cycle. In the essence, our framework supports the end-users and IoT application developers to design innovative IoT applications by reducing the programming efforts, by identifying relevant events and by suggesting potential actions based on complex event processing and reasoning for cross-domain IoT applications. TYPE OF PAPER AND