TY  - JOUR
AU  - De Vito, Saverio
AU  - D’Elia, Gerardo
AU  - Ferlito, Sergio
AU  - Di Francia, Girolamo
AU  - Davidović, Miloš
AU  - Kleut, Duška
AU  - Stojanović, Danka
AU  - Jovašević-Stojanović, Milena
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12046
AB  - Scalable and effective calibration is a fundamental requirement for Low Cost Air Quality Monitoring Systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensors specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in Air Quality (AQ) monitoring networks. Concept and sensor drift often force calibration process to be frequently repeated. These issues lead to unbearable calibration costs which denies their massive deployment when accuracy is a concern. In this work, We propose a zero transfer samples, global calibration methodology as a technological enabler for IoT AQ multisensory devices which relies on low cost Particulate Matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning concepts and can be universally applied to all units of the same type. A multi season test campaign shown that, when applied to different sensors, this methodology performances match those of state of the art methodology which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded on board of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long range data transfer needs.
T2  - IEEE Transactions on Instrumentation and Measurement
T1  - A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments
SP  - 1
EP  - 1
DO  - 10.1109/TIM.2023.3331428
ER  - 

@article{
author = "De Vito, Saverio and D’Elia, Gerardo and Ferlito, Sergio and Di Francia, Girolamo and Davidović, Miloš and Kleut, Duška and Stojanović, Danka and Jovašević-Stojanović, Milena",
year = "2023",
abstract = "Scalable and effective calibration is a fundamental requirement for Low Cost Air Quality Monitoring Systems and will enable accurate and pervasive monitoring in cities. Suffering from environmental interferences and fabrication variance, these devices need to encompass sensors specific and complex calibration processes for reaching a sufficient accuracy to be deployed as indicative measurement devices in Air Quality (AQ) monitoring networks. Concept and sensor drift often force calibration process to be frequently repeated. These issues lead to unbearable calibration costs which denies their massive deployment when accuracy is a concern. In this work, We propose a zero transfer samples, global calibration methodology as a technological enabler for IoT AQ multisensory devices which relies on low cost Particulate Matter (PM) sensors. This methodology is based on field recorded responses from a limited number of IoT AQ multisensors units and machine learning concepts and can be universally applied to all units of the same type. A multi season test campaign shown that, when applied to different sensors, this methodology performances match those of state of the art methodology which requires to derive different calibration parameters for each different unit. If confirmed, these results show that, when properly derived, a global calibration law can be exploited for a large number of networked devices with dramatic cost reduction eventually allowing massive deployment of accurate IoT AQ monitoring devices. Furthermore, this calibration model could be easily embedded on board of the device or implemented on the edge allowing immediate access to accurate readings for personal exposure monitor applications as well as reducing long range data transfer needs.",
journal = "IEEE Transactions on Instrumentation and Measurement",
title = "A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments",
pages = "1-1",
doi = "10.1109/TIM.2023.3331428"
}

De Vito, S., D’Elia, G., Ferlito, S., Di Francia, G., Davidović, M., Kleut, D., Stojanović, D.,& Jovašević-Stojanović, M.. (2023). A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments. in IEEE Transactions on Instrumentation and Measurement, 1-1.
https://doi.org/10.1109/TIM.2023.3331428

De Vito S, D’Elia G, Ferlito S, Di Francia G, Davidović M, Kleut D, Stojanović D, Jovašević-Stojanović M. A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments. in IEEE Transactions on Instrumentation and Measurement. 2023;:1-1.
doi:10.1109/TIM.2023.3331428 .

De Vito, Saverio, D’Elia, Gerardo, Ferlito, Sergio, Di Francia, Girolamo, Davidović, Miloš, Kleut, Duška, Stojanović, Danka, Jovašević-Stojanović, Milena, "A Global Multi-Unit Calibration as a Method for Large Scale IoT Particulate Matter Monitoring Systems Deployments" in IEEE Transactions on Instrumentation and Measurement (2023):1-1,
https://doi.org/10.1109/TIM.2023.3331428 . .

TY  - JOUR
AU  - Stojanović, Danka
AU  - Kleut, Duška
AU  - Davidović, Miloš
AU  - de Vito, Saverio
AU  - Jovašević-Stojanović, Milena
AU  - Bartonova, Alena
AU  - Lepioufle, Jean-Marie
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12957
AB  - Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.
T2  - Thermal Science
T1  - Low-processing data enrichment and calibration for PM2.5 low-cost sensors
VL  - 27
IS  - 3 Part B
SP  - 2229
EP  - 2240
DO  - 10.2298/TSCI221109221S
ER  - 

@article{
author = "Stojanović, Danka and Kleut, Duška and Davidović, Miloš and de Vito, Saverio and Jovašević-Stojanović, Milena and Bartonova, Alena and Lepioufle, Jean-Marie",
year = "2023",
abstract = "Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.",
journal = "Thermal Science",
title = "Low-processing data enrichment and calibration for PM2.5 low-cost sensors",
volume = "27",
number = "3 Part B",
pages = "2229-2240",
doi = "10.2298/TSCI221109221S"
}

Stojanović, D., Kleut, D., Davidović, M., de Vito, S., Jovašević-Stojanović, M., Bartonova, A.,& Lepioufle, J.. (2023). Low-processing data enrichment and calibration for PM2.5 low-cost sensors. in Thermal Science, 27(3 Part B), 2229-2240.
https://doi.org/10.2298/TSCI221109221S

Stojanović D, Kleut D, Davidović M, de Vito S, Jovašević-Stojanović M, Bartonova A, Lepioufle J. Low-processing data enrichment and calibration for PM2.5 low-cost sensors. in Thermal Science. 2023;27(3 Part B):2229-2240.
doi:10.2298/TSCI221109221S .

Stojanović, Danka, Kleut, Duška, Davidović, Miloš, de Vito, Saverio, Jovašević-Stojanović, Milena, Bartonova, Alena, Lepioufle, Jean-Marie, "Low-processing data enrichment and calibration for PM2.5 low-cost sensors" in Thermal Science, 27, no. 3 Part B (2023):2229-2240,
https://doi.org/10.2298/TSCI221109221S . .