Research on the Development of a Building Model Management System Integrating MQTT Sensing
Existing building management systems face critical limitations in real-time data integration, primarily relying on static models that lack dynamic updates from IoT sensors. To address this gap, this study proposes a novel system integrating MQTT over WebSocket with Three.js visualization, enabling real-time sensor-data binding to Building Information Models (BIM). The architecture leverages MQTT's lightweight publish-subscribe protocol for efficient communication and employs a TCP-based retransmission mechanism to ensure 99.5% data reliability in unstable networks. A dynamic topic-matching algorithm is introduced to automate sensor-BIM associations, reducing manual configuration time by 60%. The system's frontend, powered by Three.js, achieves browser-based 3D visualization with sub-second updates (280-550 ms latency), while the backend utilizes SpringBoot for scalable service orchestration. Experimental evaluations across diverse environments-including high-rise offices, industrial plants, and residential complexes-demonstrate the system's robustness: Real-time monitoring: Fire alarms triggered within 2.1 s (22% faster than legacy systems). Network resilience: 98.2% availability under 30% packet loss. User efficiency: 4.6/5 satisfaction score from facility managers. This work advances intelligent building management by bridging IoT data with interactive 3D models, offering a scalable solution for emergency response, energy optimization, and predictive maintenance in smart cities.
Authors
- Liming Zhou (ORCID: https://orcid.org/0000-0003-3156-627X)
- Ziang Wang
- Changsheng Guan (ORCID: https://orcid.org/0000-0002-9674-4471)
- Xiao Han (ORCID: https://orcid.org/0000-0003-4003-9359)
- Daiguang Fu
Institutions
- Wuhan University of Technology (CN)
- Changjiang Water Resources Commission (CN)
- Ministry of Water Resources of the People's Republic of China (CN)
Publication Details
- Journal
- Sensors
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3390/s25196069
- Citations
- 1
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.49