Yun & Licina (4) explored the importance of strategically placing stationary air quality sensors in office environments to monitor better personal exposure to pollutants like CO2, PM2.5, and PM10. Controlled experiments found that proximity to occupants and ventilation strategies significantly influence exposure detection accuracy. They showed placements near the occupant's breathing zone, can improve the prediction of inhalation exposure, especially during different activity levels. simulated the impacts of the placement of CO2 and volatile organic compounds (VOCs) sensors, their accuracy, and the numbers required for effective IAQ monitoring in four typical offices. The spatial variation of concentrations for both sensors was not large, (standard deviation CO2= 3-10% and VOCs= 21% when its emissions sources were highly variable across the space). Errors due to sensor accuracy were found to be more significant than the spatial distribution of sensors, suggesting that improving sensor quality is more critical than increasing the number of sensors. Azizi et al. examined the effects of 18 bundled multi-sensor devices (CO2 and passive infrared (PIR) radiation) positioned in three single-occupant offices (six sensors in each office) on the sensor data. They found the PIR sensors on the ceiling resulted in unreliable data, with 60% accuracy. Installing sensors under office desks can boost accuracy to 84%. Filios et al. focused on using virtual sensing and machine learning techniques to continuously adjust gas sensor locations in two indoor settings (with HVAC and naturally ventilated room). The method reduced the need for excess hardware and improved real-time air quality assessment by predicting less valuable sensor data. Their results demonstrated that improvements in data coverage and machine learning inputs could enhance prediction accuracy. Xiang et al. proposed a hybrid network combining stationary sensors for accuracy and mobile sensors for coverage. Using pollutant prediction models, their approach reduces network error by 40.4% and improves exposure measurement accuracy by 35.8% compared to stationary networks. Borodinecs et al. ( ) evaluated the importance of proper ventilation in ensuring accurate CO2 sensor measurements in indoor spaces and found the sensors perform reliably in mechanically ventilated buildings, but inaccuracies in naturally ventilated spaces, particularly where room occupancy fluctuates. Cheng et al. ( ) optimized temperature and CO2 sensor placement for multi-zone thermal comfort and IAQ monitoring using building information modeling (BIM), computational fluid dynamics (CFD), and genetic algorithms (GA). The study emphasizes that traditional sensor placement guidelines are insufficient for capturing critical spatial variations, and more sensors, especially in areas like corridors without HVAC systems, are needed to enhance thermal comfort and indoor air quality.