## 3 Materials and Methods
### IoT-Driven Methodology  
This work follows an IoT-driven methodology, as shown in Figure 2a, supported by previous knowledge and work [47; 48]. The methodology steps are as follows: \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode** & **Description** & **Key Objectives** \\ \hline Defensive & Buildings as shelters from a hostile world, focusing on safety (structural, fire, intrusion). & Safety (structural, fire, intrusion) \\ \hline Projective & Human imagination and transcendence, creating structures with aesthetic and symbolic purposes & Aesthetics, scale, illumination \\  & (e.g., temples, monuments). & Rational analysis and prediction, with objectives of rationality, order, circulation, proportion, composition \\ \hline Thermodynamic & Focus on thermal comfort and efficiency through energy consumption, adapting the environment to humans. & Energy consumption, comfort, efficiency \\ \hline Subjective & Isolation and hygiene to protect the self, with emphasis on aesthetic and visual appeal in architecture. & Isolation, filtering, control, regulation, airtightness \\ \hline Complex & Interconnected systems adapting to and influencing the environment, emphasising feedback and identity. & Interconnection, relationship, feedback \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Modes of inhabiting. Systematic overview and key objectives. Identification of buildings, regarding several key performances: orientation (north, south, east, and west), thermal variations, use of patterns (schedules, timetables), etc. Figure 2b shows smart campus, representative for extrapolating other SBE;
2. Selection of ubications, see Figure 2c, following several key characteristics: location (floor, sector, building, campus), use (classroom, study room, office, laboratory, etc. ), size (large, medium, small), occupancy (high, medium, low), among others. 3. Deployment of sensor infrastructure. The IoT ecosystem of this work includes +200 geolocated wireless sensors that collect +1000 real-time measurements every hour. Figure 2d illustrates sensor examples with the following characteristics and metrics:
* VS121 www.milesight.com/iot/product/lorawan-sensor/vs121 (accessed on 20 December 2024): occupancy smart sensor for anonymous people counting data to monitor and understand space utilisation;
* AM307 www.milesight.com/iot/product/lorawan-sensor/am319 (accessed on 20 December 2024): Indoor Air Quality (IAQ) sensors to detect CO\({}_{2}\) concentration, temperature, humidity, light, HCOH/O3 levels, TVOC, atmospheric pressure, PM 2.5, PM10 and motion;
* QPA1004 mall.industry.siemens.com/mall/es/es/Catalog/Product/S55720-S453 (accessed on 20 December 2024): room air quality sensor to detect CO\({}_{2}\) concentration, temperature and humidity;
* EM500 www.milesight.com/iot/product/lorawan-sensor/em500-pt100 (accessed on 20 December 2024): industrial temperature equipment equipped with a high-precision PT100 sensor;
* Khomp Meteo www.khomp.com/iot/en/produto/endpoint-lora-station-meteorological (accessed on 20 December 2024), meteorological station that combine 7 sensors to capture temperature, humidity, rain, wind speed and direction, ultraviolet radiation and atmospheric pressure;
4. Acquisition of experimental measurements. The key IoT variables monitored in this work are as follows: temperature (indoor, building and outdoor), CO\({}_{2}\), occupancy, humidity, light level, among others. This set is added to more external variables (to obtain SBE context-aware information) such as lecture schedule, weather conditions, architectural norms, heating on/off times, thermal thresholds, energy cost, among others;
5. Analysis of results. This step enables multidisciplinary studies; this work, within international directives about smart energy efficiency, follows the current legislation:
* RDL 486/1997 [49], which states that 'indoor temperature in spaces where sedentary work typical of offices or similar activities is performed must be between 17 and 27 \({}^{\circ}\)C'.