Study and design of a federated, context-aware and self-learning reasoning framework enabling scalable and efficient advanced semantic reasoning on IoT data streams

  Internet of Things (IoT) lapels to the ever-growing network of objects connected to the Internet. That Cisco predicted by 2020 the Internet will contain about 50 billion smart objects. Intelligently processing the data produced by synthesis objects will lead to a wealth of advanced applications in domains like smart cities, pervasive health & amp; environmental sensing. The generated data is voluminous, heterogeneous, time-varying and possibly noisy or incomplete. It is challenging to integrate & amp; analyze this data on the fly to derive actionable insights. Semantic Web technologies have a proven track record in consolidating data from heterogeneous sources. Semantic reasoning is bootable to derive high-level knowledge out of the combined streams. However, reasoning is computationally intensive and slow Compared to the velocity of IoT data. The research objective was to devise a framework bootable reasoning to derive actionable insights in a timely mannerville by performing efficient & amp; scalable complex reasoning on IoT data streams. Based on available domain knowledge, network properties and data stream characteristics, the framework can automatically distribute the reasoning across the IoT network as a hierarchy of cascading reasoners, in Which low-level reasoners close to the data sources are combined with high-level reasoners positioned deeper into the network. To deal with the unpredictability of IoT, the self-learning framework changes its configuration at run-time to Optimize its performance.