Interoperable Framework and Dynamic Web Platform for Integrated Knowledge Management in Iot-enabled Smart Water Networks
Singh, Mandeep (2026) Interoperable Framework and Dynamic Web Platform for Integrated Knowledge Management in Iot-enabled Smart Water Networks. Doctoral thesis, Birmingham City University.
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Mandeep Singh PhD Thesis_Final Version_Final Award January 2026.pdf - Accepted Version Download (18MB) |
Abstract
The integration of Internet of Things (IoT) technologies at the physical layer of Smart Water Networks (SWNs), such as smart sensors and valve controllers, enables the establishment of cohesive overlay networks within SWNs. Networked IoT systems support the utilisation of diverse data types, including numerical values, symbols, text, images, and contextualised information, thereby facilitating advanced sensing capabilities that extend beyond the initial coverage areas of SWNs. However, using publicly accessible IoT data poses interoperability challenges, including heterogeneous data representation formats, varying semantic models, and the need to adapt domain-specific standards and ontologies.
To address these challenges, this research first introduces a Data Information Representation Model (DIRM) specifically designed for the water domain. Subsequently, a Data Information Interoperability Model and Methodology (DIIM) is proposed to facilitate both syntactic (structural) and semantic (meaningful) interoperability between applications. Additionally, a Data Information Interoperability Framework (DIIF) is introduced, which leverages Semantic Web (SW) and neural Natural Language Processing (NLP) technologies to support the key steps of DIIM: transformation, alignment, storage, and validation. The transformation step converts semi-structured IoT data into a Resource Description Framework (RDF) graph using a graph converter. The alignment step matches IoT terms used to label measurement values with corresponding terms in other application data models, standards, and ontologies. The storage step records all alignments as pairs of terms in a separate ontology and annotates their references within the IoT graph model. These processes are implemented through the extensible components of DIIF, including the DIIM Semantic Similarity Scoring Tool (DS3T) and the Semantic Similarity Scoring Ontology (S3O), which support multiple semantic similarity algorithms to address various semantic aspects. For the validation step, DIIF introduces the Data Information Interoperability Ontology (DIIO) as a knowledge base and the Data Information Interoperability Questionnaire (DIIQ) to validate both syntactic and semantic interoperability between applications.
The effectiveness of DIIM and DIIF was demonstrated through implementation and validation in case studies from the water and telecommunications domains. In all validation scenarios, data interoperability was enabled and enhanced by representing data in Knowledge Graphs and by identifying similar terms within domain-specific standards and ontologies, which facilitated rapid adaptation of the required data structures and semantics.
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