IoT Sensing for Green Buildings: An Assessment of Indoor Environmental Risk in Vulnerable Occupants

Green buildings not only operate as energy-efficient structures but are also enriched with various sensors and network-supported environments. These are capable of sustaining high indoor environmental quality. This paper presents a simplified deterministic simulation framework and an extended simulation scenario for assessing how IoT-monitored green buildings may reduce indoor environmental risk for occupants with chronic health vulnerabilities. The model is motivated by previous work on green residences, housing conditions, and their relationship to physical and psychological health, where indoor air quality, ventilation, thermal comfort, humidity, noise, lighting, dust, chemical exposure, and smoke are treated as relevant housing-related factors. A baseline simulation with a virtual duration of 100 days was conducted using 100 code-generated participants distributed across two building categories: a bad climate house (BCH) and a green monitored house (GMH). Three chronic-condition profiles were taken into consideration: asthma, chronic obstructive pulmonary disease, and anxiety/stress. This prototype version of the developed Octave-compatible code allows the baseline behavior of the proposed risk model to be examined directly. The simulation protocol included nine indoor environmental indicators and a normalized risk score ranging from 0 to 100, with values classified as low, moderate, or high risk. An extended simulation was then introduced to examine a broader scenario. This extension added a normal house (NH) as an intermediate building category and expanded the health-condition set from three to five assigned profiles by including allergies and depression/low well-being. Controlled building-profile and health-profile deviations were also included, while allergies and depression/low well-being were parameterized with building-specific coefficients. This allowed the model to examine not only the separation between the two extreme cases but also the intermediate role of a partially controlled building scenario. The results showed a clear difference in risk response between the building categories. In the basic scenario, the BCH produced high final risk scores, whereas the GMH achieved low final risk scores. At the building level, the final mean risk was approximately 100.00 for the BCH and 9.37 for the GMH. In the extended simulation, the aggregated final mean risk was 99.08 for the BCH, 44.95 for the NH, and 9.02 for the GMH. These results show a clear building-level risk stratification, i.e., high risk for the adverse building case, moderate risk for the intermediate building case, and low risk for the green monitored building case. These findings should be interpreted as deterministic and extended simulation results under predefined normalized environmental profiles, not as direct clinical or field validation. The proposed code strategy is not intended as a clinical diagnostic tool, but as an oriented modeling approach for future IoT-based green building assessment, digital twin integration, and risk-aware environment management.