Field Reconstruction-Based Non-Rendezvous Calibration for Low Cost Mobile Sensors

Low-cost air pollution sensors (LCS) deployed on urban vehicles (e.g., taxis, buses) have emerged as a cost-effective solution for fine-grained air pollution monitoring. However, these mobile LCSs suffer from measurement drifting in real-world scenarios, necessitating a post-deployment real-time calibration. Unfortunately, the limited availability of urban real reference stations (RRS) restricts the calibration opportunities for LCSs. This paper proposes a non-rendezvous method that addresses this challenge by establishing virtual reference stations (VRS), which offer additional calibration opportunities for LCSs. Through the air pollution field reconstruction, the readings of VRSs are inferred from RRSs’ data. Furthermore, a confidence assessment mechanism is developed to quantify the uncertainty of established VRSs. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method, showcasing a 25% improvement over the advanced baseline.