# IoT Open-Source Architecture for the Maintenance of Building Facilities
## 6 Discussion and Conclusions  
Cloud services for data analytics, including platforms for data visualization, are now available from providers like Microsoft Azure, Amazon Web Services and Google Cloud. These services can gather data and generate alerts, notifications, and graphs, but they are not flexible and cannot interact with the BIM model or sensor data. This paper introduced a fully automated integrated framework for the maintenance of building facilities using open source IoT technologies. The IoT part of the proposed framework is composed of sensors to be installed in building facilities and wireless sensor network nodes that continuously send received data through gateways to the local and cloud servers according to configured sampling frequencies. The proposed fault detection methodology was integrated into the server and sends alarms to the end user or managers when any anomaly occurs for it to be fixed effectively; BIM was utilized to view the monitored HVAC FC component's condition and the room's physical parameters such as temperature, humidity, and luminosity using wireless-connected remote devices. A case study was used to test the new framework implementation. The integration of an FC monitoring system into the BIM model would improve the building's maintenance plan by helping the facility managers to inspect the monitored building environments inside the 3D model. Thus, facility managers can profit from the proposed framework to resolve maintenance problems in the following aspects: (a) using building facility anomaly or failure signals, facility managers can plan and schedule maintenance work in advance; (b) conditional and real-time data coming from sensors allow for more accurate maintenance; (c) data visualization and real-time data on the dashboard create the possibility of avoiding the risk of disastrous breakdowns and reduce unplanned forced outages of building components; (d) IoT sensor data for building components within the BIM model makes maintenance work more convenient, e.g., it would be easy to find the location of the failure component in the real-time BIM model; and finally, (e) collected indoor and facility sensor data is the initial tool to perform predictive maintenance actions. The study conducted in this paper aims to fill the gaps of the following researches: limited number of sensors [23], an absence of automation [24], and lack of data acquisition system [25]. The final results of the fully automated framework composed of IoT sensors, dashboard, IoT and BIM integrated application, and implemented preventive  
Figure 16: Final result of implemented heatmap function to the fan coil data and BIM. maintenance methodology of the building facilities on the server prove the proposed framework's applicability. Nevertheless, this work has several limitations, which are as follows:  
* In terms of building monitored facilities, the study is fairly restricted. Additional research is needed to perform a large-scale inquiry by connecting more building facilities and evaluating the system's integration with facility managers and clients to assess its reliability, repeatability, robustness, and simplicity of use. * A small number of sensors were used to test the proposed framework in this study. Other sensors, such as indoor air quality sensors (e.g., an oxide gas sensor, a particle dust sensor, etc.) and facility management sensors can be added to the system (e.g., motion sensor, occupancy sensor, etc.). The developed system should also be tested with a larger number and range of sensors. Future research work will attempt to use collected data from the proposed system for predictive maintenance management of building facilities. Decision support systems for facility predictive maintenance management systems, artificial intelligence (AI), specifically machine learning (ML) tools, and algorithms should be used. Time series sensor data, either historical failures, anomalies, or both, data can be used as ML inputs.