A Semantic IoT Early Warning System for Natural Environment Crisis Management

An Early Warning System or EWS is a core type of data driven IoT (Internet of Things) system used for environment disaster risk and effect management. The potential benefits of using a semantic type EWS include easier sensor and data source plug-and-play, simpler, richer and more dynamic metadatadriven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWS's are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model. We use 'lightweight' semantics for metadata to enhance rich sensor data acquisition. We use 'heavyweight' semantics for top level W3C OWL ontology models describing multi-levelled knowledge-bases and semanticallydriven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a deployed EWS