## 2 Literature
### Monitoring and Controlling Real-Life Buildings
This not only serves the purpose of enabling enhanced control, but also increases the availability of on-site measurement data, e.g., for in-depth monitoring and model calibration [37]. Energy saving potentials of sensor and control networks in the U.S. building sector were analyzed in [38]. According to [PERSON] et al. [38], the annual final energy savings in the year 2030 are estimated at 5% of the total final energy consumption in the U.S. building sector of 2018. Moreover, enabling advanced (model-based) control and human-in-the-loop control, the resulting overall energy savings are expected to be even 2-3-times higher. Hence, equipping buildings with sensor and control networks that go beyond the control of AHUs and VAV units, therefore, promises a significant reduction in the final energy consumption in this area [36; 38]. Depending on the building structure and envelope type, either wired or wireless sensors and actuators, or a combination of both, are used [27; 39]. As retrofitting measures in existing buildings are costly, in the case of existing buildings, wireless devices are preferred [39]. Compared to wired sensors, wireless devices can be deployed and installed easily. However, wired sensor and control networks, in general, tend to be more reliable and secure [38; 40]. Consequently, there is a wide range of sensor and actuator types, technologies, and communication protocols. Table 1 gives an overview of the most common protocols used for sensor and control networks in the building sector. From Table 1, it becomes obvious that multiple different technologies can coexist. This leads to higher implementation hurdles with an increasing number of devices due to increased efforts for device provisioning, bookkeeping, and registration [38]. Similarly, data collection and distribution architectures gain importance [3; 10]. Hence, recent publications deal with the implementation of suitable _information and control technologies_ (ICT) in buildings: [PERSON] et al. [37] investigate a setup based mainly on LoRaWAN [41] and 1-Wire [42] components, comprising 112 temperature and humidity sensors in three different buildings. To gather sensor data, they used Node-RED [43], in combination with a time-series database. In [44], the authors present an approach for connecting an existing BACS based on BACnet with a monitoring service in the cloud using FIWARE [45]. They also depict how cloud-based control with FIWARE is implemented, showcasing a controller applied to a virtual room. To implement model-based control for a laboratory with an area of 27 m\({}^{2}\), the authors in [46; 47] equipped the room with several sensors, including air multisensors, occupancy sensors, and meters. Heater control set-points were implemented through actuators attached to distributed fan coil units. Field-level communication was handled by a dedicated gateway and data was stored in a central database. In addition to the application of model-based control, room users were also granted access to a monitoring dashboard showing air quality measures and energy use statistics. Communicating these indicators to users can contribute to reducing energy wastage in buildings by making the users aware of the energy-related effects of their behavior. Some sources also report the need to develop specific metering devices for in situ tests. For example, refs. [48; 49] design and apply measurement devices to deal with refurbishment in ancient buildings and the specific need of condensation monitoring.