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Speaker:
Title:
Interoperable IoT Early Warning Systems
Abstract:
Disaster Risk Reduction (DRR) is a systematic approach to analyze potential disasters and reduce their occurrence rate and possible impact. The main DRR component is an Early Warning System (EWS), which is a distributed information system that is able to monitor the physical world and issue warnings if abnormal situations occur. EWSs that use Internet-of-Things (IoT) technologies (IoT EWS) are suitable to realize (near) real-time data acquisition, risk detection and message brokering between data sources and information receivers, comprising both humans (e.g., emergency managers) and machines (e.g., sirens).
Although IoT technologies offer possibilities to improve the EWS efficiency and effectiveness, this potential can only be exploited if interoperability challenges are addressed at all levels. We focus on how to improve the semantic interoperability of IoT EWSs, which refers to the ability of two or more EWSs (or EWS components) to share data elements in a prescribed format (syntax) and precise unambiguous meaning (semantics). From a literature review on semantic IoT EWS approaches, we selected the three major challenges that need to be addressed together:
1) semantic integration of a variety of data sources that make use of different standards, ontologies and data models;
2) near-real-time processing in time- and safety-critical applications; and
3) data analysis for effective situation awareness and decision support.
In this talk I will present the “SEmantic Model-driven development for IoT Interoperability of emergenCy serviceS” (SEMIoTICS) framework, which is a holistic approach for semantic IoT EWS that addresses these challenges.
