# Wireless Sensor Networks for Building Information Modeling
###### Abstract  
Building Information Modeling (BIM) is a critical element for the \"digitalization\" of the construction industry and can be exploited for energy-driven renovation procedures of existing residences. Advancing beyond a BIM with data-capturing capabilities that are limited to building static information only requires sensor data streams related to indoor/outdoor ambient conditions, as well as to energy-consumption parameters of the residences. The data streams require the deployment of robust Wireless Sensor Networks (WSNs) that are able to capture and transmit real-time data to appropriate cloud-based renovation toolkits. The technology and topology of such networks are addressed herein. The paper sets the lines for similar installations that are required by the construction industry for collecting dynamic data, since it is based on the outcome of real-world WSN installations in pilot sites in three European countries, carried out in the context of a major collaborative BIM research project. An application example of the WSN data is also provided in the context of training occupant behavior models in order to demonstrate the use of the measured data. Building Information Modeling; Wireless Sensor Networks; Z-Wave +
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The availability and use of large volumes of BIM-generated data to gain knowledge that can be further exploited in various applications [4] in a BIM-based project by using machine learning (ML) and deep learning (DL) is a new research area of particular interest for the AEC industry [5, 6]. BIM models for each stage can be developed from different data sources (e.g., designer inputs, CAD as-build, etc. ), and then relevant data can be extracted from the BIM model in order make adaptive/smart decisions. Early ML-based BIM research dealing with how the BIM process enhances the project-progress tracking was proposed in Reference [7]. Work in References [8, 9, 10] integrated cloud BIM, IoT and Data Mining (DM) into the digital twin framework, establishing a high degree of automation. Combining BIM and expert systems in order to provide a way to conduct autonomous safety risk identification is examined in Reference [11]. Predictive maintenance based on BIM, IoT and ML techniques to improve the facility management of existing building assets is examined in Reference [12]. The usability of dynamic monitoring and control for Building Energy Management Systems is presented in Reference [13] by integrating facilities, sensors and ML combined with simulation models, historical data and an intelligent controller. In Reference [14] is presented a BIM-based DM method that analyzes thermal performance of buildings with the help of BIM data; Heating, Ventilating and Air-Conditioning (HVAC); environmental conditions; and occupant behavior. An extensive literature review on machine-learning applications to BIM is out of the scope of this paper; hence, the interested reader is referred to the excellent work presented in Reference [15]. The accuracy of the BIMER energy Key Performance Indicator (KPI) values is highly affected by the level of detail of the input data, and recent studies have shown that the occupant-behavior data constitute the major cause of uncertainty [16]. Hence, a deeper understanding and proper modeling of the occupant behavior are of paramount importance. For capturing information that goes beyond static data of BIM, the BIMERR toolkit requires specific dynamic sensor data in order to generate occupant behavior (OB) models/profiles, provided in a standardized format (occupant behavior standardized XML-obXML) [17].