Abstract
Cities are constantly experiencing unprecedented growth globally, such that 56% of the world population (4.4 billion people) live in urban areas, potentially increasing 1.5 times by 2045. The impact of urban population growth is multifaceted, presenting numerous challenges related to sustainability and resilience, necessitating innovative approaches for managing and analysing diverse urban data streams. City Information Modelling (CIM) has emerged as a digital technology that integrates various digital technologies, such as Building Information Modeling (BIM), Geographic Information Systems (GIS), and the Internet of Things (IoT), to create a comprehensive and interconnected representation of urban environments. The dynamic data within the CIM can be acquired through Internet of Things (IoT) technology. However, despite the growth of the IoT, it is confronted with numerous challenges such as privacy, security, data, and energy management, and at the top of it is interoperability. To address the challenge of interoperability within IoT systems, it's essential to establish a standardized framework for exchanging information and communication protocols. This framework would alleviate the fragmentation among IoT devices, providing a common ground for seamless integration. This thesis presents the development and implementation of Unified CIM as a comprehensive framework to address these challenges. The study explored mixed research methods encircling literature review, case study and experimental methodology to propose a framework which cuts across three steps: the alignment of the IFC and SAREF sensor model, the integration of the THERCOM app into semantic interoperability framework (SIF), and the integration of the user feedback of the THERCM app into the QGIS.The study explored the Information Assigned to the Device structure-based ontology matching (IAD-SOM) technique to align the Ifc:sensor models with the saref:sensor data models. The study leveraged the basic Information that defines the sensor (Device), such as class, properties, relations, attributes, geometry, and interaction. The ontology results indicate that saref: Sensor and IfcSensor have similarities and differences in their properties and interactions. The alignment between SAREF and IFC ontologies has shown several key findings related to sensor interactions, attributes, and properties exploring strong, moderate and low similarity. In terms of sensor interactions, some mappings demonstrated strong alignment- saref:isUsedFor↔IfcRelAssociatesClassification and saref:controlsProperty↔IfcRelAssignsToControl. Several pairs exhibited moderate similarity such as the: saref:hasFunction ↔IfcRelAssignsToControl, saref:hasProfile ↔ IfcRelDefinesByType, saref:accomplishes ↔ IfcTask and saref:offers ↔IfcRelDefinesByType. Some of the pairs showed very minimal similarity or below the threshold, Such as: saref:unitOfMeasure ↔ IfcPropertySet / IfcPropertySetTemplate, saref:makesMeasurement ↔ IfcPerformanceHistory, saref:consistsOf ↔ IfcRelConnectsPortToElement / IfcRelConnectsPorts. For sensor attributes, the comparison of sensor attributes between SAREF and IFC models revealed low similarity scores in several classes, indicating limited direct interoperability between the two systems. When it comes to sensor properties, there were strong alignments in specific areas, the saref:hasSensorType ↔ Pset_SensorTypeCommon and saref:hasState ↔ Pset_Condition. Based on the threshold, the moderate match is where saref:hasManufacturer ↔ Pset_ManufacturerTypeInformation and saref:hasTimestamp ↔ Pset_SensorPHistory. These alignments exemplify great implications for Urban Smart Traffic Management System and Smart Building Management System.Furthermore, the study leveraged the SIF methodology encircling creating a user account, downloading and instantiating the generic adapter (GA), registering the GA, instantiating the knowledge engine, building the service-specific adapter (SSA) for the service and testing the SSA with the GA instance, to integrate the THERCOM app with the SIF. The data points from the App, such as the temperature and humidity sensor, smart plug, smart valve, thermoset and smart meter, were SAREFized and exposed to the SIF. The Potential of THERCOM integration into SIFS holds significant implication in CIM such as Smart Infrastructure Integration, Responsive Public Spaces, User-Centric Services, Data Integration and Service Interoperability, Efficient Resource Management, Scenario Simulation and Predictive Analysis, etc and hold great value for urban policy development towards Data Privacy and Security Policies, Service Certification and Compliance, etc.
The significance of the integration with SIF lies in semantic interoperability, a Centralized Registry for Smart Services, a Common Interface for Data Exchange, and streamlining IoT information exchange and communication protocol. Also, the user feedback from the App was integrated into the GIS, exploring the QGIS open-source software for seamless integration and enhancing decision-making processes in the urban context. These integrations and semantic alignment provide a comprehensive framework that supports the bases for UCIM for seamless interoperability and information exchange for the IoT which is a fundamental technology in capturing the dynamic information of the CIM. Through a systematic approach, this research contributes to advancing CIM as a powerful tool for managing urban data heterogeneity, promoting interoperability, and engaging stakeholders in sustainable urban development initiatives. Furhermore, this study contributes to Semantic Interoperability in Smart Cities through the Interconnect Framework, data standardization and Information Exchange protocol alignment, integrating User's Thermal Comfort Feedback into GIS Data, and semantic Interoperability on the IoT: Aligning IFC and SAREF Sensor Data Models. The findings offer valuable insights for urban designers, planners, and stakeholders seeking to leverage data-driven approaches for enhancing the resilience and sustainability of cities.
| Date of Award | 11 Dec 2024 |
|---|---|
| Original language | English |
| Awarding Institution |
|
| Supervisor | Farzad Rahimian (Supervisor) & Sergio Rodriguez (Supervisor) |