Single-node low-cost IoT sensor to measure indoor environmental quality with high spatial resolution
Year: 2026
Type: conference-paper
Abstract: Measuring thermal comfort is crucial for ensuring optimal indoor environmental quality and workplace productivity. Traditional assessment relies on dedicated microclimate monitoring stations that provide reliable but single-point measurements, failing to capture spatial gradients caused by radiant asymmetry, such as those from large glazed walls. To address this limitation, this study introduces a novel methodology utilizing a single, wall-mounted, low-cost(IR) sensor array to reconstruct high-resolution, full-room32 × 24 infrared Predicted Mean Vote (PMV) fields. The core contribution is a ray-casting analytical framework that maps two-dimensional raw pixel temperatures onto three-dimensional room surfaces via a pinhole camera model. By applying view-factor modelling, the system computes continuous Mean Radiant Temperature (MRT) and PMV fields across a 35×62 spatial grid. Experimental validation against an independent microclimate station over a week-long period demonstrated strong agreement, achieving a Mean Absolute Error (MAE)2> 0.77, and Lin’s Concordance Correlation Coefficient > 0.89 for both seated and standing models. The results revealed significant spatial comfort gradients of up to ∆PMV ≈ 0.5 across the room depth during peak solar hours, driven by the elevated temperature of the glazed wall—a heterogeneity invisible to conventional single-point sensors. Ultimately, this research validates that a single low-cost IR sensor, combined with rigorous ray-tracing radiometric modelling and empirical calibration, offers a highly accurate and scalable solution for mapping spatial indoor thermal comfort.
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203758285