Water utility decision support through the semantic web of things. (April 2018)

• Record Type: Journal Article
• Authors: Howell, Shaun; Rezgui, Yacine; Beach, Thomas
• Is Part Of: Environmental modelling & software. Volume 102(2018)
• Page Start: 94
• Page End: 114
• Publication Date: 2018-04
• Subjects: Water management -- Decision support tool -- Interoperability -- Big data -- Ontology -- Semantic web -- Internet of things -- Smart water networks; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.01.006
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Urban environments are urgently required to become smarter. However, building advanced applications on the Internet of Things requires seamless interoperability. This paper proposes a water knowledge management platform which extends the Internet of Things towards a Semantic Web of Things, by leveraging the semantic web to address the heterogeneity of web resources. Proof of concept is demonstrated through a decision support tool which leverages both the data-driven and knowledge-based programming interfaces of the platform. The solution is grounded in a comprehensive ontology and rule base developed with industry experts. This is instantiated from GIS, sensor, and EPANET data for a Welsh pilot. The web service provides discoverability, context, and meaning for the sensor readings stored in a scalable database. An interface displays sensor data and fault inference notifications, leveraging the complementary nature of serving coherent lower and higher-order knowledge. Highlights: Proposes software for interoperability and decision support in smart water systems. The paper extends IoT concepts and standards through semantic web technologies. A comprehensive smart water ontology and rule base was developed and validated. The value of semantic inference is demonstrated on a pilot site in Wales.