Enhancing the Granularity of Spatial Sensing Using a Low-Cost IoT-Based Sensing Network

Indoor carbon dioxide (CO2) concentration is a widely used indicator of ventilation effectiveness, occupancy-driven emissions, and indoor environmental quality, with direct implications for health, comfort, and cognitive performance. As demand increases for continuous and spatially resolved indoor CO2 monitoring, low-cost sensing systems integrated with advanced modeling techniques offer a promising alternative to traditional, sparsely deployed reference instruments. This study presents a hybrid fixed-mobile, low-cost Internet-of-Things (IoT) sensing framework to reconstruct high-resolution spatiotemporal indoor CO2 distributions from limited sets of sensing data collected using fixed-mobile sensing systems. After validating individual sensor nodes against reference instrumentation, a distributed sensing network was deployed in a controlled indoor environment using a combination of continuously operating stationary sensors and sequentially repositioned portable devices. A grid-based stop-and-measure protocol was adopted to ensure measurement stability while achieving full spatial coverage under sparse and asynchronous sampling conditions. To reconstruct CO2 concentration fields from incomplete spatiotemporal observations, Gaussian Process (GP) and Random Forest (RF) models were evaluated under random, periodic, and spatial cross-validation schemes. Results show that GP consistently outperformed RF in terms of variance preservation, spatial generalization, and normalized error metrics, aligning with ASTM D5157-19 recommendations for indoor air quality model evaluation. This work demonstrates that combining low-cost hybrid sensing with uncertainty-aware spatiotemporal modeling provides a scalable and cost-effective approach for indoor CO2 monitoring. Future work will extend the framework with autoregressive spatiotemporal formulations to better capture short-term temporal dependence and localized transient peaks under occupied conditions, varying ventilation states, and real-time digital twin integration.