Data Management for Building Information Modelling in a Real-Time Adaptive City Platform
This addresses interoperability and real-time data processing challenges in smart buildings, enabling faster adoption of IoT technologies, though it appears incremental as it builds on existing BIM and sensor concepts.
The paper tackles the problem of legacy Building Information Modelling (BIM) systems being unable to handle high-volume, real-time IoT sensor data, which limits timely decision-making in smart buildings, and proposes a data architecture for an Adaptive City Platform to integrate BIM, Building Management Systems, and sensor data for real-time analysis.
Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.