Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References

Abstract

There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO2, and 4 ppb for O3 could be achievable with a few mobile references in urban environments.

Keywords

• Air quality monitoring
• calibration
• gas sensor
• Internet of Things
• low-cost
• online learning