Center for Research in Energy Systems Transformation (CREST)

Indoor environmental quality (IEQ) monitoring is a critical task in building operation, maintenance, and diagnosis. Current approach based on static sensor network is not scalable for IEQ assessment that relies on costly sensing instruments. The study proposes to leverage autonomous mobility to reduce sensing infrastructure cost and enable real-time high-granularity monitoring that can be otherwise inhibitively laborious. Unique to the autonomous mobile sensing methodology, the collected IEQ samples are highly sparse in both spatial and temporal domains. The study develops spatio-temporal (ST) interpolation methods based on ST binning, global trend extraction, and local variation estimation, which efficiently use the data to construct accurate depiction of the indoor environment evolution. The method is evaluated by a standard protocol for ventilation assessment, where the estimation is shown to be highly correlated with the ground truth, and reveals the true ventilation conditions.