Joint Contactless Temperature, Humidity, and Occupancy Sensing via Wi-Fi Channel State Information on ESP32 Nodes

Smart buildings increasingly rely on dense instrumentation to monitor indoor temperature, humidity, and occupancy for energy-efficient HVAC control, yet conventional sensor deployments incur significant hardware, wiring, and maintenance costs. This paper proposes a joint contactless sensing framework that estimates ambient temperature, relative humidity, and occupancy state from Wi-Fi Channel State Information (CSI) using low-cost ESP32 nodes. Building on refractive-index based models of microwave propagation, we extend the Gladstone–Dale relation to incorporate water vapor density and derive sensitivity expressions that link multi-subcarrier CSI amplitude and phase to both temperature and humidity. We then exploit human-induced multipath perturbations and Doppler signatures to infer room occupancy without dedicated motion sensors. A multi-task learning pipeline is developed in two stages: (1) hybrid CSI feature extraction using statistical descriptors and discrete wavelet transform coefficients across 30 OFDM subcarriers, and (2) a shared-gradient boosting backbone with three task-specific heads for temperature regression, humidity regression, and occupancy classification. Experiments with ESP32-WROOM-32 devices in a climate chamber (15–35◦C, 30–80% RH) and three real-world indoor environments (laboratory, office, residential) demonstrate that the proposed approach achieves mean absolute error (MAE) of 0.72◦C for temperature, 4.6% for relative humidity, and 95.1% F1-score for binary occupancy detection, with 47 ms inference latency on-device. Compared to single-task baselines trained separately for each variable, the multi-task model improves temperature MAE by 13% and humidity MAE by 18%, while maintaining comparable occupancy accuracy. A 21 day deployment shows stable performance under routine human activity, HVAC cycles, and moderate Wi-Fi interference, with drift-controlled operation via daily calibration. The system enables cost-effective, maintenance-light ambient monitoring and occupancy-aware control using existing Wi-Fi infrastructure, reducing the need for dedicated environmental and motion sensors in smart buildings.