Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network

Walter Fuertes; Alyssa Cadena; Jenny Torres; Diego Benítez; Freddy Tapia; Theofilos Toulkeridis

doi:10.1007/978-3-030-11890-7_6

Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network

Walter Fuertes^*
, Alyssa Cadena
, Jenny Torres
, Diego Benítez
, Freddy Tapia
, Theofilos Toulkeridis

^*Corresponding author for this work

Ingeniería Electrónica y Automatización

Universidad de las Fuerzas Armadas ESPE
Escuela Politecnica Nacional
Universidad San Francisco de Quito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data Mining techniques have been applied in order to support subsequent decision-making strategies. For normalization and modeling, we applied the CRISP-DM methodology using the Pentaho Data Integration. Then, the Sap Lumira has been applied in order to acquire models of tables and views. For the data analysis, R-Studio has been used. For validation, Clustering has been applied using the k-means algorithm by the Jambu method, where it has been proceeded to check the consistency of these, being later stored and debugged in PostgreSQL. Results demonstrate that the increase in air pollutants is directly related to the traffic hours, which may cause an increase of asthma or sick related syndrome in the population. This analysis may also serve as a source of information to authorities for improving public policies in such matter.

Original language	English
Title of host publication	Information Technology and Systems - Proceedings of ICITS 2019
Editors	Manolo Paredes, Carlos Ferrás, Álvaro Rocha
Publisher	Springer Verlag
Pages	57-67
Number of pages	11
ISBN (Print)	9783030118891
DOIs	https://doi.org/10.1007/978-3-030-11890-7_6
State	Published - 2019
Event	International Conference on Information Technology and Systems, ICITS 2019 - Quito, Ecuador Duration: 6 Feb 2019 → 8 Feb 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	918
ISSN (Print)	2194-5357

Conference

Conference	International Conference on Information Technology and Systems, ICITS 2019
Country/Territory	Ecuador
City	Quito
Period	6/02/19 → 8/02/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Air pollution
Business Intelligence
Data Mining
Data analytics
Pattern recognition
Wireless sensor network

Access to Document

10.1007/978-3-030-11890-7_6

Cite this

Fuertes, W., Cadena, A., Torres, J., Benítez, D., Tapia, F., & Toulkeridis, T. (2019). Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network. In M. Paredes, C. Ferrás, & Á. Rocha (Eds.), Information Technology and Systems - Proceedings of ICITS 2019 (pp. 57-67). (Advances in Intelligent Systems and Computing; Vol. 918). Springer Verlag. https://doi.org/10.1007/978-3-030-11890-7_6

@inproceedings{7bb407ed4a1f461b85421b35c3826679,

title = "Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network",

abstract = "Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data Mining techniques have been applied in order to support subsequent decision-making strategies. For normalization and modeling, we applied the CRISP-DM methodology using the Pentaho Data Integration. Then, the Sap Lumira has been applied in order to acquire models of tables and views. For the data analysis, R-Studio has been used. For validation, Clustering has been applied using the k-means algorithm by the Jambu method, where it has been proceeded to check the consistency of these, being later stored and debugged in PostgreSQL. Results demonstrate that the increase in air pollutants is directly related to the traffic hours, which may cause an increase of asthma or sick related syndrome in the population. This analysis may also serve as a source of information to authorities for improving public policies in such matter.",

keywords = "Air pollution, Business Intelligence, Data Mining, Data analytics, Pattern recognition, Wireless sensor network",

author = "Walter Fuertes and Alyssa Cadena and Jenny Torres and Diego Ben{\'i}tez and Freddy Tapia and Theofilos Toulkeridis",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; International Conference on Information Technology and Systems, ICITS 2019 ; Conference date: 06-02-2019 Through 08-02-2019",

year = "2019",

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isbn = "9783030118891",

series = "Advances in Intelligent Systems and Computing",

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}

Fuertes, W, Cadena, A, Torres, J, Benítez, D, Tapia, F & Toulkeridis, T 2019, Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network. in M Paredes, C Ferrás & Á Rocha (eds), Information Technology and Systems - Proceedings of ICITS 2019. Advances in Intelligent Systems and Computing, vol. 918, Springer Verlag, pp. 57-67, International Conference on Information Technology and Systems, ICITS 2019, Quito, Ecuador, 6/02/19. https://doi.org/10.1007/978-3-030-11890-7_6

Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network. / Fuertes, Walter; Cadena, Alyssa; Torres, Jenny et al.
Information Technology and Systems - Proceedings of ICITS 2019. ed. / Manolo Paredes; Carlos Ferrás; Álvaro Rocha. Springer Verlag, 2019. p. 57-67 (Advances in Intelligent Systems and Computing; Vol. 918).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network

AU - Fuertes, Walter

AU - Cadena, Alyssa

AU - Torres, Jenny

AU - Benítez, Diego

AU - Tapia, Freddy

AU - Toulkeridis, Theofilos

PY - 2019

Y1 - 2019

N2 - Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data Mining techniques have been applied in order to support subsequent decision-making strategies. For normalization and modeling, we applied the CRISP-DM methodology using the Pentaho Data Integration. Then, the Sap Lumira has been applied in order to acquire models of tables and views. For the data analysis, R-Studio has been used. For validation, Clustering has been applied using the k-means algorithm by the Jambu method, where it has been proceeded to check the consistency of these, being later stored and debugged in PostgreSQL. Results demonstrate that the increase in air pollutants is directly related to the traffic hours, which may cause an increase of asthma or sick related syndrome in the population. This analysis may also serve as a source of information to authorities for improving public policies in such matter.

AB - Air pollution is a problem that causes adverse effects, which tends to interfere with human comfort, health or well-being, and that may cause serious environmental damage. In this regard, this study aims to analyze large data sets generated by real-time wireless sensor networks that determine different air pollutants. Business Intelligence and Data Mining techniques have been applied in order to support subsequent decision-making strategies. For normalization and modeling, we applied the CRISP-DM methodology using the Pentaho Data Integration. Then, the Sap Lumira has been applied in order to acquire models of tables and views. For the data analysis, R-Studio has been used. For validation, Clustering has been applied using the k-means algorithm by the Jambu method, where it has been proceeded to check the consistency of these, being later stored and debugged in PostgreSQL. Results demonstrate that the increase in air pollutants is directly related to the traffic hours, which may cause an increase of asthma or sick related syndrome in the population. This analysis may also serve as a source of information to authorities for improving public policies in such matter.

KW - Air pollution

KW - Business Intelligence

KW - Data Mining

KW - Data analytics

KW - Pattern recognition

KW - Wireless sensor network

UR - https://www.scopus.com/pages/publications/85061373107

U2 - 10.1007/978-3-030-11890-7_6

DO - 10.1007/978-3-030-11890-7_6

M3 - Contribución a la conferencia

AN - SCOPUS:85061373107

SN - 9783030118891

T3 - Advances in Intelligent Systems and Computing

SP - 57

EP - 67

BT - Information Technology and Systems - Proceedings of ICITS 2019

A2 - Paredes, Manolo

A2 - Ferrás, Carlos

A2 - Rocha, Álvaro

PB - Springer Verlag

T2 - International Conference on Information Technology and Systems, ICITS 2019

Y2 - 6 February 2019 through 8 February 2019

ER -

Fuertes W, Cadena A, Torres J, Benítez D, Tapia F, Toulkeridis T. Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network. In Paredes M, Ferrás C, Rocha Á, editors, Information Technology and Systems - Proceedings of ICITS 2019. Springer Verlag. 2019. p. 57-67. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-11890-7_6

Data Analytics on Real-Time Air Pollution Monitoring System Derived from a Wireless Sensor Network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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