IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING Received 1 September 2014; revised 27 February 2015; accepted April 25, 2015. Date of publication 17 May, 2015; date of current version 10 June, 2015. Digital Object Identifier 10.1109/TETC.2015.2432742 A Semantic IoT Early Warning System for Natural Environment Crisis Management STEFAN POSLAD 1 , (Member, IEEE), STUART E. MIDDLETON 2 , (Senior Member, ACM), FERNANDO CHAVES 3 , RAN TAO 1 , OCAL NECMIOGLU 4 , AND ULRICH BÜGEL 3 1 School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, U.K. 2 Department of Electronics and Computer Science, University of Southampton IT Innovation Centre, Southampton SO16 7NS, U.K. 3 Information and Knowledge Management Systems, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe 76131, Germany 4 Boğaziçi University, Istanbul 34342, Turkey CORRESPONDING AUTHOR: S. POSLAD (stefan.poslad@qmul.ac.uk) This work was supported in part by the European FP7 Funded Project TRIDEC under Grant 258723, in part by the other project partners in helping to deliver the complete project Syste, in particular, GFZ, and in part by the German Research Centre for Geosciences, Potsdam, Germany. The work of R. Tao was supported by the Queen Mary University of London for a Ph.D. studentship. ABSTRACT An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) 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 metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs 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 Web Ontology Language ontology models describing multileveled knowledge-bases and semantically driven 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 infrastructure. INDEX TERMS Early warning system, Internet of Things, crisis management, semantic Web, scalable, time-critical, resilience. I. INTRODUCTION A. MOTIVATION AND CHALLENGES Natural environment disasters may be caused by natural haz- ard events, such as tsunamis, or by manmade hazard events such as earth substrate drilling. These may in turn cause widespread natural environment damage that can take the affected regions years to recover from, following the onset of the disaster. An Early Warning System or EWS is a core type of IoT information system used for environment disaster risk and effect management. It helps prevent loss of life and reduces the economic and material impact of disasters [1]. In 2011, it has been estimated that the cost of installing an EWS for tsunami detection in the Indian Ocean was between $30 to $200 million dollars, depending on the number of sensor buoys used, the precision of the measurements; and that the benefit to cost ratio was 4:1, i.e., every dollar spent on mitigation saved society four US dollars [2]. An EWS is distinct from other types of environment ICT monitoring systems in that it supports four main func- tions: Risk analysis of predefined hazards and vulnerabilities; Monitoring and warning by means of relevant parameters used for forecasts to generate accurate and timely warnings; Dissemination and communication of the risk information and warnings to those at risk; Response capability built upon response plans that leverage local capabilities and the preparation to react to warnings. Typically, specific parts of natural environments are instrumented with fixed sensors to monitor them. These represent IoTs in the physical environment. Examples of such instrumented environments include drilling rigs, which actively alter the natural environment, and specific regions that are monitored because they are prone to potential 246 This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ VOLUME 3, NO. 2, JUNE 2015