Enhancing Facility Management with a BIM and IoT Integration Tool and Framework in an Open Standard Environment

Abstract

Integrating building information modeling (BIM) with Internet of things (IoT) technologies significantly enhances facility management (FM) by enabling advanced real-time monitoring of indoor environmental quality (IEQ). However, technical complexity, proprietary limitations, high software costs, and unclear long-term benefits hinder practical adoption. This study suggests a way to combine BIM and IoT using open standards like IFC and JSON, simple programming tools like Node-RED, and secure cloud services. A case study of a six-story office building showed that real-time IEQ sensor data can be combined with organized BIM information, helping to make better decisions about maintaining, replacing, or upgrading heating, ventilation, and air conditioning (HVAC) systems. This integration offers essential data needed for using advanced analysis techniques, specifically tackling issues with compatibility, ease of use, and organizational challenges, which is especially advantageous for small-to-medium-sized office buildings. Nevertheless, this study faced limitations due to restricted real-time data access from existing building management systems and preliminary predictive analytic capabilities, highlighting a need for improved direct data integration and robust analytical methods in future implementations.

1. Introduction

Facility management (FM) is rapidly evolving as building information modeling (BIM) integrates with the Internet of things (IoT), enabling digital twins for real-time data management [1,2]. This integration combines static BIM data—spatial geometries, asset metadata, and detailed building component information—with dynamic IoT sensor streams, facilitating continuous monitoring of indoor environmental quality (IEQ) and informed operational decisions [3,4,5,6]. Integrating machine learning (ML) further enhances these digital twins, enabling predictive maintenance to improve occupant health, energy efficiency, and asset longevity [7,8].

However, practical BIM–IoT implementation faces significant challenges. Traditional building management systems (BMS) typically lack interoperability with BIM models, restricting advanced analytical capabilities [6,9]. Retrofitting existing buildings with IoT infrastructure presents technical and economic barriers, especially when integrating diverse sensor data into established BIM environments [10,11]. Moreover, steep learning curves frequently cause FM personnel to struggle with advanced BIM tools [12,13,14,15,16]. Organizational reluctance to invest, driven by unclear returns and significant upfront costs, further impedes widespread adoption [11,17,18,19,20]. Furthermore, although industry foundation classes (IFC)—originally developed by buildingSMART specifically for exchanging building information during the construction phase—support cross-platform interoperability, they are not inherently optimized for real-time operational data exchange. IFC files typically have large file sizes and complex structures unsuitable for efficient web-based integration, whereas JSON’s lightweight, web-compatible structure significantly improves performance and integration with IoT streams [21,22]. Converting IFC data to JSON involves critical trade-offs, potential semantic loss, and continuous parser maintenance to ensure accuracy [23]. Since BIM primarily manages static data and IoT captures dynamic but contextually limited data, integrating both becomes essential to effectively address FM’s operational requirements [3,6,12,24].

A critical area impacted by these challenges is IEQ, which directly influences occupant health, productivity, and overall well-being. Poor IEQ, often due to inadequate ventilation, pollutant exposure, and inconsistent thermal comfort, significantly affects occupants [25]. Traditional BMS typically lack the interoperability and detailed contextual data required for proactive IEQ management. BIM–IoT integration addresses this gap by incorporating IoT sensor data into BIM models, enabling continuous monitoring, advanced analytics, and informed operational strategies [26]. Global regulations increasingly mandate proactive IEQ monitoring, making such integration essential [27,28].

To address these limitations, this study introduces a vendor-neutral BIM–IoT integration framework designed explicitly for real-time IEQ monitoring and predictive maintenance. In contrast to previous XML-based IFC data conversions [21,29], this research employs JSON, currently a candidate standard by buildingSMART [30], chosen for its lightweight and web-compatible advantages. The proposed approach combines IFC-to-JSON conversions with Node-RED, a low-code platform seamlessly integrating BIM models with real-time IoT data. The resulting web-based dashboard provides intuitive, spatially contextualized visualization of IEQ metrics, significantly enhancing FM decision-making capabilities.

Prior literature on BIM maturity levels [31] highlights significant challenges in achieving unified standards essential for BIM Level 3 implementation, such as adopting a common data environment (CDE) and IFC for seamless interoperability [32,33]. Previous studies typically relied on commercial software, required high-level combined expertise in BIM and programming, rarely addressed cybersecurity explicitly, and primarily focused on real-time visualization [6,34,35,36,37,38,39]. In contrast, this research employs open standards, clear role separation, and explicitly incorporates cybersecurity protocols, directly addressing risks identified previously. Nevertheless, fully achieving BIM Level 3 maturity involves broader requirements beyond this study’s scope. This research is guided by three main objectives: (a) addressing identified technical, organizational, and usability barriers; (b) evaluating existing methodologies; and (c) validating a practical integration framework through a detailed case study.

Specifically, this research addresses the following questions:

• How can IFC-based BIM data be structured using open standards (JSON) for effective real-time integration with IoT sensor data in IEQ monitoring for FM?
• What low-code integration methods enable FM personnel to effectively use BIM–IoT solutions without advanced BIM or programming expertise?
• How does BIM–IoT integration enhance operational decision-making and predictive maintenance compared to traditional FM practices?
• How can we effectively address the primary interoperability and cybersecurity challenges associated with integrating BIM and IoT in cloud-based platforms?

This research contributes significantly to the field by providing the following:

• Enhanced data interoperability: IFC-to-JSON conversion ensures efficient cloud-based integration, facilitating the unified management of diverse datasets;
• Real-time IEQ monitoring: Node-RED’s low-code integration of live IoT sensor streams with BIM components enables real-time spatially contextualized visualization, significantly enhancing operational decisions;
• Improved usability and cross-disciplinary collaboration: an intuitive web-based dashboard facilitates seamless collaboration among BIM specialists, IT developers, and FM professionals;
• Open standards and vendor neutrality: utilizing open standards (IFC, JSON) and accessible low-code tools minimizes reliance on proprietary software, reducing costs and improving scalability.

We structure the remainder of this paper as follows: Section 2 reviews literature regarding technological, usability, organizational, and digital twin-related barriers to BIM–IoT integration. Section 3 describes the research methodology. Section 4 details the BIM–IoT integration framework, including data sources, IFC-to-JSON conversion, and Node-RED workflows. Section 5 validates the proposed framework through interoperability testing, advanced data analytics, cybersecurity measures, and comparative analysis. Section 6 discusses key findings, practical implications, and study limitations. Section 7 presents conclusions and future research suggestions.

2. Literature Review

The integration of BIM and the IoT has significant potential to enhance FM by providing detailed building information combined with real-time sensor data. Despite these advantages, the practical adoption of BIM–IoT integration faces multiple barriers, including technological, usability, organizational and workflow challenges, and advancements in Digital Twin technology.

2.1. Technological Barriers

Key technological barriers include interoperability issues and legacy BMS limitations. Traditional BMS platforms typically use proprietary protocols and isolated data architectures, complicating the integration of real-time IoT data with BIM models [6,11]. Additionally, current BIM standards such as IFC, designed initially for static design-phase data, lack adequate provisions for managing dynamic operational data, necessitating complex conversions into flexible, web-compatible formats such as JSON [21,22,29]. Specifically, IFC files, including IFC-XML, exhibit structural complexity and large file sizes, significantly reducing their performance in web-based, real-time IoT data exchanges [21,23,40]. In contrast, JSON’s lightweight structure facilitates faster data parsing, enhanced web compatibility, and seamless integration with real-time IoT data streams and web-based BIM applications [40]. However, converting IFC data into JSON involves critical trade-offs, notably the potential loss of semantic richness inherent in the original IFC schema, necessitating careful schema mapping and ongoing parser maintenance to ensure data integrity and accuracy [23]. Alternative conversion methods, such as IFC.js or glTF-based approaches, have been considered in prior research but were not selected for this study due to limitations in precise schema mapping capabilities and greater complexity in customization required for operational scenarios. Compared to IFC.js, which primarily focuses on geometry rendering and offers limited support for extracting detailed metadata—such as IfcExternalReference—or conducting tailored schema mapping required for FM workflows, the selected IFC-to-JSON pipeline enables full access to both geometric and non-geometric data using Python scripting. This allows for custom data structuring aligned with FM system requirements. Additionally, IFC.js requires client-side WebAssembly parsing of entire IFC files, which can be computationally intensive for large models. Similarly, glTF provides efficient 3D visualization but lacks inherent capabilities to retain semantic relationships and metadata essential for operational analysis, often requiring supplemental structures or extensions. These limitations reinforce the suitability of the JSON-based method adopted in this study for flexible, web-based FM integration. This study directly addresses these gaps by employing IFC-to-JSON conversion methods and leveraging the low-code platform (Node-RED), to facilitate real-time integration and interoperability.

2.2. Usability

Usability issues represent significant obstacles, as FM personnel often face steep learning curves when adapting to advanced BIM technologies, largely due to inadequate training and support [16]. A shortage of BIM-trained FM staff further hampers efficient data usage and underutilizes BIM tools [14]. These challenges are compounded by organizational resistance driven [19,20]. To mitigate these usability barriers, this introduces an intuitive, web-based dashboard designed specifically for ease of use by FM professionals without extensive BIM expertise.

2.3. Organizational and Workflow Challenges

Organizational barriers frequently arise due to insufficient managerial support, unclear roles, and ambiguous expectations regarding the return on investment from BIM–IoT integration initiatives [11,13]. These complexities significantly increase due to the long-term nature of operational tasks, the variety of stakeholders involved, and fragmented data governance structures [41,42]. While previous literature extensively addresses general technological and usability challenges in BIM–IoT integration, relatively few studies focus explicitly on challenges arising directly within the FM operational stage. The transition from construction to FM often involves a pronounced gap in clearly defined roles, responsibilities, and structured workflows, leading to the suboptimal utilization of BIM and fragmented data management [43]. This operational gap specifically hinders continuous maintenance planning, real-time performance monitoring, and effective decision-making during the FM phase. To address these challenges, this research proposes a structured and replicable integration framework that clearly defines responsibilities, standardizes workflows, and implements robust data management practices, ensuring seamless integration and sustained continuity in FM operations.

Figure 1 has been adapted from guidelines outlined in the ISO 19650 series (ISO 19650-1, ISO 19650-2), PAS 1192-2, PAS 1192-3, and typical BIM Execution Plans (BEP). The figure illustrates the handover gap between BIM management during the construction and operational phases, underscoring the necessity of defined organizational roles, structured workflows, and standardized deliverables.

2.4. Advancements in Digital Twin Technology

Recent advancements position BIM–IoT integration within the broader context of digital twin technology, which enhances capabilities such as predictive analytics and predictive maintenance [1,2,7]. Although digital twin implementations are becoming increasingly sophisticated, widespread adoption remains limited due to persistent interoperability and usability issues [8]. This paper contributes to this emerging field by providing a practical example of a scalable, vendor-neutral approach. The proposed approach utilizes open standards and accessible low-code tools to address critical gaps identified in recent literature, demonstrating its applicability and ease of adoption.

In summary, effective BIM integration in FM requires overcoming key challenges in interoperability, usability, organizational commitment, limited practical implementation, and workflow standardization. As highlighted in

Table 1. Summary of Barriers and Proposed Solutions for BIM–IoT Integration in FM.

Barrier Dimension	Identified Challenges	Proposed Solutions	References
Technological	Interoperability, legacy systems, and data formats	Open standards (IFC, JSON) and low-code integration (Node-RED)	[11,13,22,24,44]
Usability	Steep learning curves and inadequate BIM expertise among FM staff	Intuitive, web-based dashboard interfaces; targeted user training programs	[12,13,14,15,16]
Organizational and workflow	Insufficient managerial support, unclear roles, and ROI uncertainty	Structured integration frameworks, clear governance, and standardized workflows	[11,17,18,19,20]
Advancements in digital twin	Limited implementation examples and persistent interoperability issues	Vendor-neutral, scalable implementations using open standards and accessible tools	[1,7,8]

, the absence of defined roles and structured workflows at the BIM-to-FM transition stage frequently undermines FM operations. This study specifically addresses these gaps by proposing a structured integration framework that leverages open standards, intuitive dashboards, and standardized processes, thus fostering more robust, sustainable, and scalable BIM–IoT integration.

3. Research Method

This study employs a structured mixed-method approach to develop and validate a practical BIM–IoT integration framework tailored for FM, with a focus on IEQ. The methodology involves sequential phases, each with specific tools (Figure 2).

Initially, a literature review defined the study context. The problem identification phase analyzed current FM practices to pinpoint barriers to BIM–IoT integration. We collected IoT data during the development phase. The indicators (occupancy levels and CO₂) were primarily selected due to data availability constraints in our case study building. This choice demonstrates practical applicability and highlights how facility managers can effectively utilize integrated IEQ data. Furthermore, we can adapt this approach to other scenarios with different datasets. We then developed a web-based integration framework using IFC-to-JSON data conversion and low-code programming via Node-RED. The validation phase involved a detailed case study in an operational office building, integrating BIM models (IFC) with IoT sensor data (IEQ). Key tools employed included Python scripts for data processing and IFC-to-JSON conversion, Node-RED for IoT data visualization, MongoDB for structured data management, and AWS IoT Core for secure data streaming. Due to cybersecurity constraints limiting direct real-time data streaming from the BMS, this research relied on periodic CSV exports. While this approach enabled secure and practical data integration testing, it inherently restricted the evaluation of full real-time responsiveness and introduced minor delays. Future research should therefore explore secure direct real-time data integration methods to comprehensively assess system performance under actual operational conditions. Finally, this study concludes by discussing interoperability, usability, implications for FM, and future research recommendations.

4. Proposed Approach

4.1. Case Study: Office Building in Kanagawa, Japan

This study utilized a practical case study of a six-story office building located in Kanagawa, Japan. Serving dual functions, the facility operates both as a workspace for employees and as a test environment for advanced building technologies, particularly environmental sensing systems (Figure 3). Detailed BIM data and IoT sensor outputs from this building were essential for this research. This study specifically focused on the first-floor co-working space, which was selected because it provides comprehensive IoT sensor data related to IEQ and is managed and monitored through the building’s centralized BMS. These data were critical for demonstrating and validating the practical integration benefits of BIM–IoT technologies in FM.

4.2. Data Source and Collection

The data utilized in this study strictly adhere to defined BIM–IoT–FM maintenance requirements from prior literature. Data sources are categorized as either originating from BIM models or external platforms.

Table 2. Recommended Data Storage Locations for BIM-FM Maintenance Integration.

Data Category	Description	Preferred Location	Remarks	References
Asset information	Equipment details and specifications	BIM + FM system	Core data embedded in BIM; lifecycle data externally maintained	[11,17,45,46,47,48,49,50]
Maintenance records	Work orders and maintenance logs	External system	Stored and updated in CMMS; BIM serves as access gateway or index	[17,49,50,51,52,53]
Performance and sensor data	Real-time sensor data	External system	IoT/BMS capture data; BIM visualizes without storing raw data	[17,38,50,54,55,56]
Spatial information	Building geometry and floor plans	BIM	Provides authoritative spatial context	[17,49,50,57,58,59]
Documentation	Manuals, warranties, and checklists	Linked externally	Managed in EDMS/FM systems, referenced via BIM hyperlinks	[17,48,49,60,61]

outlines the recommended storage locations, categorizing each data type and providing succinct details for clarity.

The building’s BMS collected sensor data because cybersecurity policies prevented direct real-time streaming. To address this, the BMS generated CSV files at scheduled intervals containing sensor identifiers, timestamps, and measured IEQ parameters (Figure 4). A secure Node-RED workflow was implemented to periodically access and integrate these CSV files, facilitating secure data transfer and testing.

Each sensor was directly linked to specific BIM elements (e.g., rooms or defined areas) using unique GlobalIds derived from the IFC model. Sensor identifiers (sensor IDs) or zone names from the BMS were systematically matched with the corresponding GlobalIds through a structured sensor–BIM mapping table (

Table 3. Example Sensor–BIM Mapping Table.

GlobalId (IFC)	Element Name	Sensor ID	Sensor or Zone Name
0eyMYa5qz2eeRNRT2UhxQ	1st floor collaborative creation area	1.00903.204	Occupancy rates
059mVaylX87vpmyegNh8ad	1st floor library area	1.00903.205	Occupancy rates

), enabling the precise and automatic integration of IoT sensor data with BIM components for real-time spatial visualization. However, maintaining stable GlobalIds is critical in this mapping process. In IFC, the GlobalId is a unique identifier (GUID) assigned to each element upon IFC export. While it typically remains unchanged throughout the element’s lifecycle, it may be regenerated if the element is deleted and recreated during model updates. Such changes can disrupt the linkage between BIM components and external IoT or FM systems, potentially leading to data mismatches or the loss of tracking continuity. We recommend enforcing consistent modeling practices and developing a version-controlled update protocol that maintains GlobalIds across model revisions to mitigate this issue.

Additionally, Figure 5 illustrates the physical locations of all the installed sensors and their corresponding BIM elements within the studied area, depicting sensor placement and distribution. In this study, we specifically analyzed data from sensor locations SDF 101-16 in the collaborative creation area and SDF 101-28 in the library section. Occupancy rates were measured using sensors that detect human presence via infrared radiation emitted by individuals. CO₂ sensors were installed on tables at approximately 1.2 m (standard breathing height per ASHRAE guidelines) above floor level, aligning with recommended standards for accurately capturing air quality conditions within occupant breathing zones.

4.3. IFC-to-JSON Data Conversion

We created and managed the BIM models (Figure 5) in Autodesk Revit prior to the data conversion. To facilitate interoperability, these models were exported to the IFC format; specifically, IFC4 was selected because it is the latest widely adopted IFC standard, providing improved interoperability and richer property sets essential for FM. JSON was chosen due to its lightweight structure, compatibility with web platforms, and ease of integration with IoT data, adhering to internationally recognized open BIM standards. The IFC export process involved selecting appropriate export settings to ensure the retention of relevant data, including geometry, spatial relationships, and element properties.

This study specifically adopts the IFC data structure (Figure 6), originating with fundamental entities such as IfcRoot, capturing critical attributes like GlobalId and OwnerHistory. These expand into tangible building elements (IfcElement), including physical components like doors, walls, and mechanical systems. Each IFC element provides metadata essential for asset management operations. Properties associated with these elements are defined either through user-defined attributes in IfcPropertySet (e.g., materials and manufacturers) or standardized measurable parameters (area, volume, and length) through IfcElementQuantity, supporting maintenance scheduling, asset tracking, and lifecycle management decisions.

Moreover, IFC integrates external resource management via IfcDocumentReference, storing external links (URLs and file paths) and effectively connecting BIM data with IoT platforms and other FM systems, thus enhancing predictive maintenance workflows. The IFC-to-JSON conversion pipeline used the IfcOpenShell Python library (version 0.8.0 with Python 3.12) due to its robust IFC schema compliance and detailed metadata support, essential for accurate FM data management. This choice addressed key limitations found in alternative tools, such as potential semantic data loss, insufficient metadata integration, and limited customization flexibility for FM-specific needs. To facilitate lightweight, web-based BIM data integration for FM, this research implemented an IFC-to-JSON conversion pipeline using the open-source IfcOpenShell Python library. The extraction targeted three primary data categories necessary for FM workflows: asset information, spatial information, and external system references, as follows:

• Asset information (metadata and quantities): Asset metadata was collected from each IfcProduct entity using .get_info(), selectively retaining properties relevant to FM (e.g., Name, GlobalId, PredefinedType, and Tag). We extracted quantitative data embedded within IfcElementQuantity sets, such as area, volume, and length, using the following dedicated Python function (Figure 7).

Structured asset metadata and quantities were formatted within JSON, enabling direct accessibility for downstream FM systems.

• Spatial information (geometric representation): To preserve the spatial context of each asset, the script utilizes the geom.create_shape() method from IfcOpenShell’s geometry module. This method converts the 3D representation of each IfcProduct into a list of vertices and indexed triangle faces. We processed the geometry using world coordinates to ensure consistency across all elements (Figure 8).

Figure 8. Python script that converts IFC geometry for web-based 3D visualization.

Figure 8. Python script that converts IFC geometry for web-based 3D visualization.

The resulting geometric data, stored under a “points” attribute in JSON, enabled seamless integration with web-based 3D viewers for contextualized real-time sensor visualization.

• External system integration (documentation and IoT interfaces): The conversion pipeline also extracted references to external documents and systems by traversing IFC relationships (IfcRelAssociatesDocument). We captured URLs or file paths using a tailored Python function, as follows (Figure 9).

Figure 9. Python script that retrieves external document references from IFC entities.

Figure 9. Python script that retrieves external document references from IFC entities.

This functionality enables IFC elements to serve as direct access points to external documentation, sensor data platforms, and maintenance records, supporting integrated workflows that include digital logbooks, performance monitoring, and advanced analytical strategies within a unified interface. Following the IFC-to-JSON data conversion using the IfcOpenShell Python library version 0.8.0, the resulting JSON structure organizes key IFC attributes, quantity data, external document references, and simplified geometry. Figure 10 provides an illustration.

4.4. Node-RED Workflow for Data Integration

We utilize Node-RED, a visual low-code programming tool, to integrate and visualize BIM and real-time IoT sensor data on a web-based dashboard. This Node-RED workflow (Figure 11) leverages structured JSON data, aligning BIM components with sensor data streams. The Python-based scripts described facilitate the extraction and formatting of BIM data, ensuring accurate and efficient integration.

The Node-RED implementation, detailed and comprehensive, addresses the limitations of real-time IoT data access due to stringent cybersecurity restrictions, effectively demonstrating a realistic IoT data integration scenario.

• Sensor Data Retrieval and Real-Time Simulation. (1) The implementation of a practical data simulation method was necessary due to cybersecurity constraints that prevented direct real-time data streaming from the BMS. Node-RED workflows continuously retrieve periodic CSV exports from the BMS containing IEQ parameters such as CO₂ levels, occupancy, temperature, and humidity. Each CSV file is systematically parsed, converting each row into structured JSON objects, including sensor identifiers, timestamps, and measurement values. This approach effectively emulates real-time data streams, thereby enabling realistic system testing and validation without compromising data security;
• Real-Time Data Streaming. (2) The parsed sensor data is securely transmitted real-time using the MQTT protocol through AWS IoT Core, an IoT broker providing robust security features such as transport layer security (TLS) encryption and certificate-based authentication. MQTT subscription nodes (AWS IoT MQTT In) ensure secure and continuous sensor data streaming, subsequently relaying these data points to the dashboard for instantaneous visualization. This robust security architecture guarantees reliable and secure data flow between the BMS and the visualization components;
• HTTP Endpoint. (3) The HTTP endpoint (/x) within Node-RED acts as the primary data gateway for interactive client–server communication. Upon receiving a client request, structured JSON files derived from IFC-based BIM models are loaded, parsed, and processed into JavaScript objects for seamless integration. Advanced web technologies, such as Three.js and Chart.js, power sophisticated client-side visualizations that combine these data with real-time IoT sensor streams. WebSocket nodes enable real-time, bidirectional data transmission between server and client interfaces, ensuring responsive and dynamic visualization updates;
• Database Management for Historical Analytics. (4) Node-RED integrates MongoDB to handle long-term data storage and management. Two dedicated MongoDB nodes (MongoDB Sensor and MongoDB IFC) separately store sensor data and BIM-related metadata, respectively. This structured storage allows comprehensive historical analyses, enabling advanced analytics and machine-learning-based predictive maintenance. Consequently, facility managers benefit from actionable insights derived from historical data, significantly enhancing predictive operational capabilities.

4.5. Web-Based BIM–IoT Integration

The proposed BIM–IoT integration framework (Figure 12) presents a comprehensive and robust approach for integrating BIM data with real-time IoT sensor streams, significantly enhancing FM operations. Initially, we export detailed BIM models developed in Revit to the IFC4 format, an internationally recognized open standard that ensures interoperability. These IFC files are subsequently converted into structured JSON data through a Python-based parser, extracting essential spatial geometries, hierarchical structures (e.g., IfcBuilding and IfcSpace), and unique GlobalIds. The JSON data are then stored on servers or directly embedded within web-based applications, supporting dynamic and interactive visualizations of building elements alongside their associated sensor data. Node-RED, a visual programming tool, is utilized to integrate and visualize BIM and IoT datasets seamlessly. Real-time IoT sensor data—such as temperature, humidity, occupancy, and CO₂ levels—are securely streamed via AWS IoT Core, an IoT broker employing MQTT protocol with TLS encryption and certificate-based authentication. This method ensures secure, reliable, and real-time data transmission. Mapping IoT sensor identifiers to the BIM GlobalIds accomplishes sensor data integration, enabling the precise spatial representation and contextual visualization of environmental parameters.

Building upon the IFC-to-JSON data conversion and Node-RED workflows, the proposed BIM–IoT integration framework consolidates BIM spatial information with real-time IoT sensor data into a dynamic and interactive web-based visualization. The resulting web-based dashboard, developed using Node.js and HTML, provides interactive 3D models and time-series graphs. Additionally, heat maps illustrate spatial correlations, such as variations in CO₂ concentrations across different building zones. Historical sensor data, securely stored in MongoDB via Node-RED, facilitates advanced analytics, machine learning applications, and predictive maintenance strategies, significantly enhancing data-driven facility management decision-making.

5. Validation of the Proposed Framework

5.1. Interoperability and Dashboard Functionality

Extensive testing was conducted to validate the interoperability and functionality of the developed integration dashboard. This evaluation focused on assessing effective integration across diverse data sources. (Figure 13), the web-based dashboard provided real-time, spatially contextualized visualization of IoT sensor data within the BIM environment, confirming robust interoperability. The dashboard also included fundamental analytical tools, such as heatmaps, illustrating spatial variations in environmental parameters.

5.2. Advanced Data Integration

The proposed framework not only visualizes IoT sensor data within the BIM environment but also consolidates multidisciplinary datasets into a centralized, cloud-based repository. This unified approach facilitates advanced analytical processes, including ML, enhancing predictive decision-making capabilities (Figure 14).

A correlation analysis identified a moderate positive relationship between occupancy and CO₂ levels (r = 0.30), confirming occupancy as a notable influencing factor. Integrating occupancy data with BIM spatial information supports precise predictions and targeted proactive maintenance. Facility managers can thus readily identify spaces consistently experiencing elevated CO₂ concentrations due to high occupancy and implement strategic infrastructure enhancements, such as increased ventilation capacity or additional sensor deployment (Figure 15).

To further validate the practical utility of the proposed integration, a linear regression analysis was performed on IoT sensor data, examining the relationship between occupancy and indoor CO₂ concentrations. After data cleaning, normalization, and splitting into training (80%) and testing (20%) datasets, the regression analysis yielded a coefficient of 1.4049, indicating a 1.4 ppm increase in CO₂ per additional occupant, with an intercept of 555.61 ppm (Figure 16).

The effectiveness and reliability of the BIM–IoT integration framework heavily depend on the quality and accuracy of the IoT sensors used. Sensor inaccuracies, data drift, or inconsistent calibration practices can significantly limit the practical benefits of real-time IEQ monitoring and predictive analytics. Facility managers should, therefore, adopt rigorous sensor calibration schedules, implement sensor redundancy, and conduct periodic validation audits to maintain data integrity and reliability.

However, the moderate R² value (0.0897) indicates a weak linear relationship under current conditions and does not yet demonstrate strong predictive capability. This limited result is likely due to minimal operational variation in the relatively new building used for the case study. Nonetheless, this preliminary analysis demonstrates the potential for data-driven diagnostics. Further data collection across longer timeframes or in older buildings with more pronounced occupancy fluctuations could yield stronger predictive insights. This result supports proactive HVAC adjustments, such as filter replacements or architectural modifications [62,63,64]. Consequently, this BIM–IoT integration framework presents tangible operational advantages, particularly addressing contemporary challenges like PM2.5 accumulation common in older buildings requiring ventilation upgrades.

5.3. Cybersecurity and Data Integrity

Cybersecurity and data integrity were validated according to the NIST Cybersecurity Framework, a widely recognized guideline developed by the U.S. National Institute of Standards and Technology (NIST). This framework systematically addresses cybersecurity risks through five core functions: identify, protect, detect, respond, and recover (Figure 17).

Identify: Initial audits identified critical data flows, IoT devices, and potential vulnerabilities, including insecure channels and insufficient authentication mechanisms.

Protect: A multi-layered security approach was implemented, comprising the following:

• Layer 1 (sensor data): IoT sensors are secured with TLS encryption and certificate-based authentication to prevent data interception and sensor spoofing;
• Layer 2 (cloud communication): AWS IoT Core secured through MQTT with TLS encryption, certificate-based authentication, and access controls to protect against unauthorized data access;
• Layer 3 (integration and processing): Node-RED secured via HTTPS, API-key authentication, and role-based access control to prevent unauthorized access to workflows;
• Layer 4 (data storage): MongoDB uses encryption-at-rest and implements strict access controls to prevent data theft and unauthorized access to the database.
• Layer 5 (user dashboard): web interfaces secured with HTTPS and API-key authentication to protect against session hijacking and unauthorized access.

Detect: continuous real-time monitoring through AWS IoT Core and audit logging within Node-RED enabled the immediate identification of anomalies or security threats.

Respond: Node-RED’s modular design facilitated rapid incident response and quick adjustments to workflows, effectively mitigating identified threats.

Recover: structured encrypted database storage and regular backups ensured swift restoration and operational continuity following any disruptions or cybersecurity incidents.

Validation tests confirmed that these cybersecurity measures were fully aligned with NIST guidelines. These measures significantly enhance the reliability and trustworthiness of the proposed BIM–IoT integration framework.

5.4. Comparative Validation

To verify the effectiveness of the proposed BIM–IoT integration approach, this section compares it directly against prominent limitations identified in recent studies. Four critical aspects—software dependency, required expertise, added value beyond visualization, and cybersecurity—are discussed, demonstrating how this research specifically addresses and improves upon these challenges (

Table 4. Comparative Validation of the Proposed Approach against Previous Studies.

Research Aspect	Previous Studies		This Research
Software dependency	[36,37,39,65,66]	Previous methods relied on commercial software, causing high recurring costs and vendor dependency	Employs open standards (IFC, JSON) and open-source tools (Node-RED), reducing vendor dependency and operational costs
Expertise requirements	[36,37,39,65,66]	Most existing methods required personnel with combined high-level expertise in both BIM modeling and programming, limiting practical usability and implementation	Clearly defines and separates roles (BIM modeling, data integration, facility management), reducing combined skillset requirements
Value beyond visualization	[6,34,35,36,37,65]	Limited to real-time visualization	Demonstrates benefits of integrating real-time IoT data with BIM for advanced maintenance analytics
Cybersecurity considerations	[6,34,35,36,37,38,39]	Rarely addressed cybersecurity explicitly, potentially increasing risks of data breaches or system vulnerabilities	Concern, employs security protocols (TLS encryption, certificate-based authentication)

).

The comparative analysis indicates that this research addresses several limitations noted in prior studies. While previous approaches have demonstrated strong visualization capabilities and valuable insights, many have relied on commercial software or required high-level technical expertise. This study builds upon those foundations by emphasizing open standards, simplified workflows, and enhanced usability. However, we should interpret the present comparison as complementary rather than conclusive, as future research is necessary to benchmark performance across diverse operational contexts.

6. Discussion

Existing research on BIM–IoT integration for FM primarily emphasizes monitoring and improving IEQ [6,34,35,36,37,39,65,66]. Despite these advances, significant limitations remain, the reliance on proprietary software like Autodesk Revit and Dynamo, leading to substantial long-term operational costs [36,37,39,65,66]. Such recurring costs often discourage facility operators, as these investments typically do not directly contribute to revenue generation during the operational lifecycle. Another barrier is the combined BIM and programming expertise traditionally required from FM personnel, which is both rare and expensive, complicating practical implementation during operations

To address these industry gaps, the proposed workflow (Figure 18) separates roles aligned with specific expertise across the building lifecycle. Initially, BIM specialists convert IFC-based models into structured JSON files before operational handover, aligning closely with the early-stage employer’s information requirements (EIR). This structured preparation simplifies data handovers from construction to operations, making BIM deliverables immediately usable by FM personnel. Additionally, understanding FM data requirements early in the design and construction phases ensures clarity in project execution, promoting careful preparation of essential information and avoiding unnecessary BIM model complexity. This early awareness significantly enhances overall project planning and coordination.

Furthermore, recognizing FM data requirements early in the design and construction phases fosters clarity in project execution. This proactive understanding encourages the thorough preparation of vital information and helps avoid unnecessary complexity in the BIM model. Such early awareness significantly improves overall project planning and coordination. During operations, IT staff use low-code platforms (e.g., Node-RED, HTML, and JavaScript) to integrate and visualize real-time IoT sensor data. Consequently, FM personnel can effectively leverage their domain expertise without needing extensive BIM or programming training, bridging the BIM–FM skills gap.

Previous studies often limit their scope to real-time visualization, rarely demonstrating significant added value beyond traditional BMS capabilities [6,34,35,36,37,65]. In contrast, this research integrates real-time IoT data with predictive analytics. Although the regression analysis between occupancy and CO₂ levels was preliminary, it demonstrates the potential for data-driven diagnostics. Future machine learning implementations within this framework could extend toward anomaly detection, predictive analytics for maintenance scheduling, and optimizing operational efficiency, thus opening avenues for deeper analytical exploration.

The proposed BIM–IoT integration framework is also adaptable for broader FM applications beyond occupancy and CO₂ measurements. It can readily incorporate additional sensors monitoring parameters such as lighting, noise, humidity, PM2.5, or equipment vibration, facilitating broader facility management scenarios like preventive equipment maintenance, space utilization optimization, and occupant comfort improvement. Its reliance on open data standards (JSON) and flexible integration tools (Node-RED) ensures adaptability, easy customization, and compliance with various future standards or certification requirements. Integrated data can further support the predictive maintenance of electrical and mechanical systems [54], improve security monitoring via spatially mapped sensor data [6], and optimize space utilization through real-time occupancy analytics [36]. Moreover, incorporating diverse sensor types—such as those for lighting control, water usage, and fire safety—further enriches a facility manager’s capability for comprehensive, data-driven decision-making [6,36]. The structured JSON data format and low-code integration tools (e.g., Node-RED) offer significant flexibility and scalability, considerably broadening the framework’s practical applicability across multiple FM scenarios. Explicitly adopting Open BIM standards—in this study, IFC and JSON—significantly enhances interoperability and reduces dependency on proprietary solutions. Open BIM facilitates seamless data exchange and collaboration across the architecture, engineering, construction, and operations (AECO) industry, resulting in lower long-term operational costs, increased flexibility, and improved sustainability of BIM–IoT integration throughout the building lifecycle.

The framework’s structured JSON format and low-code tools provide flexibility and scalability, significantly broadening practical applicability across diverse FM scenarios. Additionally, the approach facilitates smoother BIM-to-FM handovers post-construction, supports renovations and retrofitting projects, and enhances asset management throughout the building lifecycle.

While validated with existing sensors in the current case study, the framework allows the seamless updating of BIM models and sensor mappings within Node-RED when new sensors are installed, emphasizing flexibility and adaptability. However, the validity of findings remains context-specific due to limited testing in a newly constructed building without active FM personnel. Future studies should involve mature facilities and active FM teams to ensure more robust operational validation.

Cybersecurity remains another critical yet often underrepresented aspect in BIM–IoT research. Given extensive data exchange and system integration, explicit cybersecurity measures such as MQTT and TLS encryption adopted here enhance reliability and applicability. However, periodic outsourcing of substantial BIM updates might introduce inconsistencies; thus, establishing standardized guidelines or certification procedures for BIM updates is essential. Additionally, the workflow highlights emerging hybrid professional roles, emphasizing the need to implement targeted training programs that blend BIM and FM expertise.

This research faced certain practical limitations. First, restricted real-time data access due to security concerns limited the study to periodic data exports from BMS. Second, the newness of the case study building (completed May 2022, data collected in 2024) and the absence of dedicated FM staff participation restricted comprehensive operational validation. Future research should explore incorporating advanced analytics into the BIM–IoT workflow to further enhance automation, decision-making, and predictive maintenance capabilities in FM.

7. Conclusions

This research presents a practical framework integrating BIM and IoT technologies, specifically designed to enhance FM through the advanced real-time monitoring of IEQ and foundational data preparation for further analysis. By converting IFC models into a lightweight JSON format—currently a provisional standard by buildingSMART—the framework addresses interoperability challenges, facilitating seamless integration with web-based platforms. However, this conversion involves trade-offs, including potential semantic simplification and schema customization requirements.

A practical office-building case study validated the framework using Node-RED, a low-code visual programming tool. The intuitive, web-based dashboard significantly improved usability, operational effectiveness, and cross-disciplinary collaboration. This research explicitly underscores the benefits of employing open standards, defined role separation, and robust cybersecurity considerations, collectively reducing software dependency, simplifying required skillsets, and ensuring data integrity, thereby addressing and overcoming key limitations identified in previous studies.

Analytical validation, particularly correlation and regression analyses linking occupancy and CO₂ levels, demonstrated preliminary but meaningful operational insights, laying the groundwork for future predictive analytics rather than establishing strong predictive capability. Despite practical constraints—including limited real-time sensor data access and validation in a recently completed building lacking active FM staff—the proposed framework demonstrates substantial practical advantages for FM decision-making. Future research should establish standardized guidelines for bi-directional BIM updates between BIM models and FM platforms to facilitate continuous and effective data utilization, along with further exploration of robust predictive analytics through extended machine learning applications. Utilizing Open BIM standards, this framework ensures long-term data interoperability and accessibility, minimizes reliance on proprietary software, and significantly improves the adaptability and sustainability of BIM integration, benefiting not only FM but the entire building lifecycle.