research-article

Data reconstruction applications for IoT air pollution sensor networks using graph signal processing

Authors:

Pau Ferrer-Cid,

Jose M. Barcelo-Ordinas,

Jorge Garcia-VidalAuthors Info & Claims

Volume 205, Issue C

https://doi.org/10.1016/j.jnca.2022.103434

Published: 01 September 2022 Publication History

Abstract

The analysis of sensor networks for air pollution monitoring is challenging. Recent studies have demonstrated the ability to reconstruct the network measurements with graphs derived from the acquired data, thus describing the complex relationships between the sensors that compose the network. In this work, we propose a graph-based data reconstruction framework that can be used to carry out different post-processing applications that arise in real-world low-cost sensor deployments for air pollution monitoring. This data reconstruction framework first describes the relationships between the different network sensors by means of a graph learned from the measured data, and then a signal reconstruction model is superimposed to reconstruct sensor data. This methodology allows reconstructing sensor data to carry out missing value imputation, signal reconstruction at points where there are no physical sensors (virtual sensing), or data fusion. The results, using real data taken with an air pollution monitoring network including reference stations and low-cost sensors, show how this framework performs well for these applications compared to estimates obtained from individual sensors and task-specific state-of-the-art methods that are not able to deal with this necessary range of applications. In short, the results show the potential of using graphs, whose topology is based on the measurements taken, to calibrate and reconstruct signals in heterogeneous networks of low-cost air pollution sensors for a wide variety of applications.

References

[1]

Aiello G., Chetta V., Del Coco M., Giangreco E., Pino S., Storelli D., A virtual augmentation for air quality measurement sensor networks in smart cities, in: 2019 IEEE International Symposium on Measurements & Networking (M&N), IEEE, 2019, pp. 1–6.

[2]

Barcelo-Ordinas J.M., Doudou M., Garcia-Vidal J., Badache N., Self-calibration methods for uncontrolled environments in sensor networks: A reference survey, Ad Hoc Netw. 88 (2019) 142–159.

[3]

Barcelo-Ordinas J.M., Ferrer-Cid P., Garcia-Vidal J., Ripoll A., Viana M., Distributed multi-scale calibration of low-cost ozone sensors in wireless sensor networks, Sensors 19 (11) (2019),.

[4]

Barcelo-Ordinas J.M., Ferrer-Cid P., Garcia-Vidal J., Viana M., Ripoll A., H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network, Data Brief 36 (2021).

[5]

Barcelo-Ordinas J.M., Garcia-Vidal J., Doudou M., Rodrigo-Muñoz S., Cerezo-Llavero A., Calibrating low-cost air quality sensors using multiple arrays of sensors, in: Wireless Communications and Networking Conference (WCNC), IEEE, 2018, pp. 1–6.

[6]

Belkin M., Matveeva I., Niyogi P., Regularization and semi-supervised learning on large graphs, in: International Conference on Computational Learning Theory, Springer, 2004, pp. 624–638.

[7]

Bigi A., Mueller M., Grange S.K., Ghermandi G., Hueglin C., Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech. 11 (6) (2018) 3717–3735.

[8]

Castell N., Dauge F.R., Schneider P., Vogt M., Lerner U., Fishbain B., Broday D., Bartonova A., Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int. 99 (2017) 293–302.

[9]

De Vito S., Esposito E., Salvato M., Popoola O., Formisano F., Jones R., Di Francia G., Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches, Sensors Actuators B 255 (2018) 1191–1210.

[10]

Dong X., Thanou D., Frossard P., Vandergheynst P., Learning Laplacian matrix in smooth graph signal representations, IEEE Trans. Signal Process. 64 (23) (2016) 6160–6173.

Digital Library

[11]

Dong X., Thanou D., Rabbat M., Frossard P., Learning graphs from data: A signal representation perspective, IEEE Signal Process. Mag. 36 (3) (2019) 44–63.

[12]

Ferrer-Cid P., Barcelo-Ordinas J.M., Garcia-Vidal J., Graph learning techniques using structured data for IoT air pollution monitoring platforms, IEEE Internet Things J. (2021) 1,.

[13]

Ferrer-Cid P., Barcelo-Ordinas J.M., Garcia-Vidal J., Ripoll A., Viana M., A comparative study of calibration methods for low-cost ozone sensors in IoT platforms, IEEE Internet Things J. 6 (6) (2019) 9563–9571,.

[14]

Ferrer-Cid P., Barcelo-Ordinas J.M., Garcia-Vidal J., Ripoll A., Viana M., Multi-sensor data fusion calibration in IoT air pollution platforms, IEEE Internet Things J. 7 (4) (2020) 3124–3132.

[15]

Friedman J., Hastie T., Tibshirani R., Sparse inverse covariance estimation with the graphical lasso, Biostatistics 9 (3) (2008) 432–441.

[16]

Fung P.L., Zaidan M.A., Sillanpää S., Kousa A., Niemi J.V., Timonen H., Kuula J., Saukko E., Luoma K., Petäjä T., et al., Input-adaptive proxy for black carbon as a virtual sensor, Sensors 20 (1) (2020) 182.

[17]

Guo S., He L., Gu Y., Jiang B., He T., Opportunistic flooding in low-duty-cycle wireless sensor networks with unreliable links, IEEE Trans. Comput. 63 (11) (2013) 2787–2802.

Digital Library

[18]

Hagan D., Isaacman-VanWertz G., Franklin J., Wallace L., Kocar B., Heald C., Kroll J., Calibration and assessment of electrochemical air quality sensors by colocation with regulatory-grade instruments, Atmos. Meas. Tech. 11 (1) (2018) 315–328.

[19]

Heimann I., Bright V., McLeod M., Mead M., Popoola O., Stewart G., Jones R., Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors, Atmos. Environ. 113 (2015) 10–19.

[20]

Jabłoński I., Graph signal processing in applications to sensor networks, smart grids, and smart cities, IEEE Sens. J. 17 (23) (2017) 7659–7666.

[21]

Kalofolias V., How to learn a graph from smooth signals, in: Artificial Intelligence and Statistics, 2016, pp. 920–929.

[22]

Liu L., Kuo S.M., Zhou M., Virtual sensing techniques and their applications, in: 2009 International Conference on Networking, Sensing and Control, IEEE, 2009, pp. 31–36.

[23]

Liu X., Wang X., Zou L., Xia J., Pang W., Spatial imputation for air pollutants data sets via low rank matrix completion algorithm, Environ. Int. 139 (2020).

[24]

Maag B., Zhou Z., Thiele L., A survey on sensor calibration in air pollution monitoring deployments, IEEE Internet Things J. 5 (6) (2018) 4857–4870.

[25]

Mateos G., Segarra S., Marques A.G., Ribeiro A., Connecting the dots: Identifying network structure via graph signal processing, IEEE Signal Process. Mag. 36 (3) (2019) 16–43.

[26]

Matusowsky M., Ramotsoela D.T., Abu-Mahfouz A.M., Data imputation in wireless sensor networks using a machine learning-based virtual sensor, J. Sens. Actuat. Netw. 9 (2) (2020) 25.

[27]

Mnih A., Salakhutdinov R.R., Probabilistic matrix factorization, Adv. Neural Inf. Process. Syst. 20 (2007).

[28]

Mondal A., Das M., Chatterjee A., Venkateswaran P., Recovery of missing sensor data by reconstructing time-varying graph signals, 2022, arXiv preprint arXiv:2203.00418.

[29]

Motlagh N.H., Lagerspetz E., Nurmi P., Li X., Varjonen S., Mineraud J., Siekkinen M., Rebeiro-Hargrave A., Hussein T., Petaja T., et al., Toward massive scale air quality monitoring, IEEE Commun. Mag. 58 (2) (2020) 54–59.

[30]

Okafor N.U., Delaney D.T., Missing data imputation on iot sensor networks: implications for on-site sensor calibration, IEEE Sensors Journal 21 (20) (2021) 22833–22845.

[31]

Ortega A., Frossard P., Kovačević J., Moura J.M., Vandergheynst P., Graph signal processing: Overview, challenges, and applications, Proc. IEEE 106 (5) (2018) 808–828.

[32]

Quinteros M.E., Lu S., Blazquez C., Cárdenas-R J.P., Ossa X., Delgado-Saborit J.-M., Harrison R.M., Ruiz-Rudolph P., Use of data imputation tools to reconstruct incomplete air quality datasets: A case-study in Temuco, Chile, Atmos. Environ. 200 (2019) 40–49.

[33]

Ribeiro G.B., Lima J.B., Graph signal processing in a nutshell, J. Commun. Inf. Syst. 33 (1) (2018).

[34]

Ripoll A., Viana M., Padrosa M., Querol X., Minutolo A., Hou K.M., Barcelo-Ordinas J.M., García-Vidal J., Testing the performance of sensors for ozone pollution monitoring in a citizen science approach, Sci. Total Environ. 651 (2019) 1166–1179.

[35]

Saukh O., Hasenfratz D., Thiele L., Reducing multi-hop calibration errors in large-scale mobile sensor networks, in: Proceedings of the 14th International Conference on Information Processing in Sensor Networks, in: IPSN ’15, ACM, New York, NY, USA, 2015, pp. 274–285.

[36]

Shuman D.I., Narang S.K., Frossard P., Ortega A., Vandergheynst P., The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Process. Mag. 30 (3) (2013) 83–98.

[37]

Spinelle L., Gerboles M., Villani M.G., Aleixandre M., Bonavitacola F., Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide, Sensors Actuators B 215 (2015) 249–257.

[38]

Spinelle L., Gerboles M., Villani M.G., Aleixandre M., Bonavitacola F., Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2, Sensors Actuators B 238 (2017) 706–715.

[39]

Williams D.E., Low cost sensor networks: How do we know the data are reliable?, ACS Sens. 4 (10) (2019) 2558–2565.

[40]

Woo J., An S., Hong K., Kim J., Lim S., Kim H., Eum J., Integration of CFD-based virtual sensors to a ubiquitous sensor network to support micro-scale air quality management, J. Environ. Informat. 27 (2) (2016).

[41]

Yu H., Li Q., Wang R., Chen Z., Zhang Y., Geng Y.-a., Zhang L., Cui H., Zhang K., A deep calibration method for low-cost air monitoring sensors with multilevel sequence modeling, IEEE Trans. Instrum. Meas. 69 (9) (2020) 7167–7179.

[42]

Zaidan M.A., Dada L., Alghamdi M.A., Al-Jeelani H., Lihavainen H., Hyvärinen A., Hussein T., Mutual information input selector and probabilistic machine learning utilisation for air pollution proxies, Appl. Sci. 9 (20) (2019) 4475.

[43]

Zaidan M.A., Motlagh N.H., Fung P.L., Lu D., Timonen H., Kuula J., Niemi J.V., Tarkoma S., Petäjä T., Kulmala M., et al., Intelligent calibration and virtual sensing for integrated low-cost air quality sensors, IEEE Sens. J. 20 (22) (2020) 13638–13652.

[44]

Zhang M.-Z., Wang L.-M., Xiong S.-M., Using machine learning methods to provision virtual sensors in sensor-cloud, Sensors 20 (7) (2020) 1836.

Cited By

Paredes-Ahumada JFerrer-Cid P Barcelo-Ordinas JGarcia Vidal J(2023)Black carbon proxy sensor model for air quality IoT monitoring networksProceedings of the 2023 International Conference on embedded Wireless Systems and Networks10.5555/3639940.3639970(237-242)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.5555/3639940.3639970
Ferrer-Cid PBarcelo Ordinas JGarcia-Vidal J(2023)Quality Aware Graph Learning Regularization For Heterogeneous Air Quality Sensor NetworksProceedings of the 2023 International Conference on embedded Wireless Systems and Networks10.5555/3639940.3639954(100-105)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.5555/3639940.3639954
Paredes-Ahumada JFerrer-Cid PBarcelo-Ordinas JGarcia-Vidal JReche CViana MHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)Robust Proxy Sensor Model for Estimating Black Carbon Concentrations Using Low-Cost SensorsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597316(1-6)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597316

Index Terms

Data reconstruction applications for IoT air pollution sensor networks using graph signal processing
1. Hardware

Index terms have been assigned to the content through auto-classification.

Recommendations

Nodes Deployment Algorithm Based on Data Fusion and Evidence Theory in Wireless Sensor Networks
Abstract
Wireless sensor networks have been widely researched and developed in recent years. The node deployment problem is a multi-dimensional nonlinear optimization problem with continuous discrete variables. In order to improve the coverage effect of ...
Trusted data fusion by using cellular automata in wireless sensor networks
CEAS '11: Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference

In this paper, we introduce a new protocol, named TDFCA (Trusted Data Fusion by using Cellular Automata) in Wireless sensor Networks. TDFCA uses Cellular Automata rules to find the most suitable cluster head, perform data fusion, and find the most ...
The reliability analysis of wireless sensor networks based on the energy restrictions

The Wireless Sensor Networks WSNs are composed of sensor nodes and sink nodes, and the network of sensor nodes is energy constrained, so the reliability of the WSNs is closely related to the energy of the nodes. According to the characteristics of the ...

Comments

Information & Contributors

Information

Published In

cover image Journal of Network and Computer Applications

Journal of Network and Computer Applications Volume 205, Issue C

Sep 2022

328 pages

ISSN:1084-8045

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Academic Press Ltd.

United Kingdom

Publication History

Published: 01 September 2022

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Paredes-Ahumada JFerrer-Cid P Barcelo-Ordinas JGarcia Vidal J(2023)Black carbon proxy sensor model for air quality IoT monitoring networksProceedings of the 2023 International Conference on embedded Wireless Systems and Networks10.5555/3639940.3639970(237-242)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.5555/3639940.3639970
Ferrer-Cid PBarcelo Ordinas JGarcia-Vidal J(2023)Quality Aware Graph Learning Regularization For Heterogeneous Air Quality Sensor NetworksProceedings of the 2023 International Conference on embedded Wireless Systems and Networks10.5555/3639940.3639954(100-105)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.5555/3639940.3639954
Paredes-Ahumada JFerrer-Cid PBarcelo-Ordinas JGarcia-Vidal JReche CViana MHossein Motlagh NZaidan MRebeiro-Hargrave AArvind D(2023)Robust Proxy Sensor Model for Estimating Black Carbon Concentrations Using Low-Cost SensorsProceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities10.1145/3597064.3597316(1-6)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3597064.3597316

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents