Data Information Interoperability Model for IoT-enabled Smart Water Networks

Syntactic and semantic interoperability is a fundamental requirement for the success of the Internet of Things (IoT)-enabled Smart Water Networks (SWNs). Still, whilst consuming publicly accessible IoT data, the syntactic and semantic representation of the collected data poses challenges for the suc...

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Bibliographic Details
Published in:2022 IEEE 16th International Conference on Semantic Computing (ICSC) pp. 179 - 186
Main Authors: Singh, Mandeep, Wu, Wenyan, Rizou, Stamatia, Vakaj, Edlira
Format: Conference Proceeding
Language:English
Published: IEEE 01.01.2022
Subjects:
Data Interoperability
Data models
Internet of Things
IoT/WoT
Ontologies
Ontology
Representation Standard
Semantic Model Generation and Alignment
Semantics
Service-oriented architecture
Smart Water Networks
Syntactic harmonization
Syntactics
Water quality
Water Quality Monitoring
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Summary:Syntactic and semantic interoperability is a fundamental requirement for the success of the Internet of Things (IoT)-enabled Smart Water Networks (SWNs). Still, whilst consuming publicly accessible IoT data, the syntactic and semantic representation of the collected data poses challenges for the success of pervasive and ubiquitous sensing in the water domain. Challenges include the heterogeneity of data representation formats, semantic models, and the adoption of domain-specific standards and ontologies. These challenges emphasise the requirement for enhanced interoperability in SWNs. To address this, we propose a Data and Information Interoperability Model (DIIM) by combining the Semantic Web technologies, widely known for overcoming interoperability issues, and Model-driven architecture (MDA) approach. DIIM facilitates syntactic inter-operability by serialization conversion and adoption of domain-specific standards as well as semantic interoperability of metadata by aligning the semantic models of IoT and Smart Water Network (SWN) applications. Furthermore, it automatically creates an ontology as a semantic model if it is missing and adds references to existing domain-specific ontologies as annotation in their models. We evaluate DIIM methodology by applying it to a real-world use case of IoT-enabled applications for water quality monitoring.
DOI:10.1109/ICSC52841.2022.00038