Towards a semantic web of things for smart cities

Realising the value of the growing quantity of webenabled devices and data is a significant global challenge, and is essential in overcoming the mounting global environmental and economic issues. This is especially true in urban environments, where the potential to leverage technology for operational performance improvements is highest, due to the high density of many interlinked systems. This thesis hypothesises that moving beyond the stateoftheart of Internet of Things technologies, to a Semantic Web of Things approach, can improve the outcomes of technology interventions for stakeholders, by improving applicationlayer interoperability. The premise is that by providing a rich and shared understanding of the cyberphysical context of devices, services, and data, applications are able to interoperate better. This in turn leads to a more integrated consideration of the problem space by business services, and so a more holistic optimisation can be achieved, across previously siloed systems. The methodology adopted was an iterative experimentation process alongside experts, culminating in the development of a Semantic Web of Things platform for smart cities. This consists of an integrated suite of APIs for accessing semanticallyenriched builtenvironment data from various perspectives. This includes an IoT resource discovery endpoint which extracts semantic metadata from a triple store and transforms it to be compliant with the recent Hypercat standard. The API also exposes a full SPARQL endpoint for rich querying of the data, as well as BIM, CityGML, and timeseries endpoints for accessing specific views of the data. To further promote resource discovery and interoperability, the platform includes a 3D GUI for visually exploring the city, building, and sensor data, and is built on a comprehensive smart city ontology which extends the recent BSI smart city ontology and aligns this with several relevant de facto standards. III To support the final design science stage and provide a rigorous exploration of the hypothesis, participatory action research methods were iteratively undertaken across 6 research projects, involving engagement with circa 40 organisations. This work considered the subdomains of smart cities, and initially focused on the energy domain. Software and ontologies were developed and analysed alongside experts, before an extended learning iteration in the water domain was undertaken, producing a smart water semantic model and platform. The work demonstrates that a Semantic Web of Things approach does improve applicationlayer interoperability. Some of the results observed through this project include i) reducing energy consumption in public buildings by circa 30% through a smart retrofit BEMS, ii) enabling water utilities to better manage regulatory compliance and network management, and iii) maximising the profitability of domestic renewable energy generation through smart holonic microgrids. Semantic technologies are well suited to addressing the ‘variety’ of big data in IoT systems, and support a system of systems approach to smart city management. Whilst existing research in this area focuses on annotating sensors with ICT descriptors, this work shows that integrating this with rich domain context is beneficial in promoting interoperability and discoverability. Future work involves investigating the use of the artefacts developed in other smart city domains, and furthering the consensusbuilding process towards standardisation