Related papers: Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices

Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices

URL: http://arxiv.org/abs/2003.12011v1
Date: Tue, 24 Mar 2020 10:26:51 GMT
Title: Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices
Authors: Saverio De Vito, Girolamo Di Francia, Elena Esposito, Sergio Ferlito, Fabrizio Formisano and Ettore Massera
Abstract summary: Low cost chemical microsensors array have shown capable to provide relatively accurate air pollutant quantitative estimations. Their accuracy have shown limited in long term field deployments due to negative influence of several technological issues. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models.
Score: 1.957338076370071
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and note-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance.

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