Building Information Modeling–based Cyber-Physical Platform for Building Performance Monitoring

International Journal of Distributed Sensor Networks, 2020

Recommended citation: Zhang, Y.Y., Kang, K., Lin, J.R.*, Zhang, J.P., Zhang, Y. (2020). Building Information Modeling–based Cyber-Physical Platform for Building Performance Monitoring. International Journal of Distributed Sensor Networks, 16(2), 1-21. doi: 10.1177/1550147720908170 cited by count


Building performance management requires massive data input; however, the relevant data are separated and heterogeneous; thus, it prevents a comprehensive building performance management. Building information modeling brings a new way to capture rich information of a building, and has great potential in data interoperability for building performance management. This article presents a scalable building information modeling–based cyber-physical platform for building performance monitoring to integrate heterogeneous data from different buildings. A smart sensor network based on Arduino and standard protocol is installed for data sensing and collection. A building information modeling–based sensing information model integrating heterogeneous data in a unified structure is proposed, and a scalable NoSQL database is established to store data in a cloud environment. A series of RESTful web services is developed to share data for building performance management applications. The proposed platform is developed taking the advantage of horizontal scalability of NoSQL database, and the data schema and services are generated automatically based on the unified data model. The platform has collected data from 77 buildings in China, and the results of a case study show the platform brings a new paradigm in collecting, storing, integrating, and sharing of sensor data and building information for building performance monitoring and analytics.

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This work was funded by the National Key R&D Program of China (grant no.: 2017YFC0704200). Dr. Lin was also supported by the Beijing Natural Science Foundation (no.: 8194067) and the Natural Science Foundation of China (no.: 51908323).

Accession Number: WOS:000517405200001

ISSN: 1550-1477

IDS Number: KR1UX

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