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An Embedded Application System for Data Collection of Atmospheric Pollutants with a Classification Approach

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Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

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Abstract

This paper shows the application of an embedded system with a wireless sensor network to collect atmospheric pollutants data obtained from sensors placed into micro-climates; such dataset provides the information required to test classification algorithms, that helps to develop applications to improve air quality in specific areas.

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References

  1. Microclimate (1993). http://www.gardening.cornell.edu/weather/microcli.pdf. Accessed 16 May 2015

  2. Amigoni, F., Brandolini, A., Caglioti, V., Di Lecce, V., Guerriero, A., Lazzaroni, M., Lombardi, F., Ottoboni, R., Pasero, E., Piuri, V., Scotti, O., Somenzi, D.: Agencies for perception in environmental monitoring. IEEE Trans. Instrum. Meas. 55(4), 1038–1050 (2006)

    Article  Google Scholar 

  3. Bertolotti, I.C., Hu, T.: Modular design of an open-source, networked embedded system. Comput. Stan. Interfaces 37, 41–52 (2015)

    Article  Google Scholar 

  4. Breiman, L.: Baggin predictors. Mach. Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  5. Le Cessie, S., van Houwelingen, J.C.: Ridge estimators in logistic regression. Appl. Stat. 41(1), 191–201 (1992)

    Article  MATH  Google Scholar 

  6. Christofides, N.: Graph Theory: An Algorithmic Approach. Computer Science and Applied Mathematics. Academic Press, London (1975)

    MATH  Google Scholar 

  7. Chung, W.-Y., Sung-Ju, O.: Remote monitoring system with wireless sensors module for room environment. Sens. Actuators, B 113(1), 64–70 (2006)

    Article  Google Scholar 

  8. Cortez, P.: Multilayer Perceptron. http://www3.dsi.uminho.pt/pcortez/mg.pdf. Accessed 16 May 2015

  9. Gao, M., Zhang, F., Tian, J.: Environmental monitoring system with wireless mesh network based on embedded system. In Fifth IEEE International Symposium on Embedded Computing, SEC 2008, pp. 174–179, October 2008

    Google Scholar 

  10. Jiawei, H., Micheline, K.: Data Mining-Concepts and Techniques. Elsevier Publishers, San Francisco (2008)

    Google Scholar 

  11. John, G., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann (1995)

    Google Scholar 

  12. Jung, Y.J., Lee, Y.K., Lee, D.G., Ryu, K.H., Nittel, S.: Air pollution monitoring system based on geosensor network. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008, vol. 3, pp. III - 1370–III - 1373, July 2008

    Google Scholar 

  13. Iba, W., Langley, P.: Induction of one-level decision trees. In: Sleeman, D., Edwards, P. (eds.) Machine Learning: Proceedings of the Ninth International Workshop, pp. 233–240. Morgan Kaufmann, San Mateo (1992)

    Google Scholar 

  14. Kelly, F.J., Fuller, G.W., Walton, H.A., Fussell, J.C.: Monitoring air pollution: use of early warning systems for public health. Respirology 17(1), 7–19 (2012)

    Article  Google Scholar 

  15. Förster, K., et al.: Incremental kNN classifier exploiting correct-error teacher for activity recognition. In: Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA), pp. 445–450 (2010)

    Google Scholar 

  16. Landwehr, N., Hall, M., Frank, M.: Logistic Model Trees. Mach. Learn. 59(1), 161–205 (2005). Springer

    Article  MATH  Google Scholar 

  17. University of Pau: Communication performance of low-resource sensor motes for data-intensive applications (1993). http://cpham.perso.univ-pau.fr/Paper/WD13.pdf. Accessed 16 May 2015

  18. Pandharipande, A., Caicedo, D.: Daylight integrated illumination control of led systems based on enhanced presence sensing. Energ. Build. 43(4), 944–950 (2011)

    Article  Google Scholar 

  19. Salata, F., et al.: How high albedo and traditional buildings’ materials and vegetation affect the quality of urban microclimate. A case study. Energy Build. 99, 32–49 (2015). Elsevier

    Article  Google Scholar 

  20. Shafik, R.A., et al.: Design considerations for reliable embedded systems. In: Swingler, J. (ed.) Reliability Characterisation of Electrical and Electronic Systems, pp. 169–194. Woodhead Publishing Series in Electronic and Optical Materials, London (2014)

    Google Scholar 

  21. Sukwon, C., Nakyoung, K., Hojung, C., Rhan, H.: Micro sensor node for air pollutant monitoring: hardware and software issues. Sensors 9(10), 7970–7987 (2009). 14248220

    Article  Google Scholar 

  22. Sumner, M., Frank, E., Hall, M.: Speeding up logistic model tree induction. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 675–683. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  23. Ting, K.M., Witten, I.H.: Stacking bagged and dagged models. In: Proceedings of 14th International Conference on Machine Learning, pp. 367–375. Morgan Kaufmann (1997)

    Google Scholar 

  24. Tsow, F., Forzani, E., Rai, A., Wang, R., Tsui, R., Mastroianni, S., Knobbe, C., Gandolfi, A.J., Tao, N.J.: A wearable and wireless sensor system for real-time monitoring of toxic environmental volatile organic compounds. IEEE Sensors J. 9(12), 1734–1740 (2009)

    Article  Google Scholar 

  25. UCI: Machine learning repository (2007). http://archive.ics.uci.edu/ml/. Accessed on 16 May 2015

  26. Venetianer, P.L., Deng, H.: Performance evaluation of an intelligent video surveillance system - a case study. Comput. Vis. Image Understand. 114(11), 1292–1302 (2010). Special Issue on Embedded Vision

    Article  Google Scholar 

  27. Libelium: Waspmote-Datasheet, Libelium Comunicaciones Distribuidas S.L., Document version V5.5 - 10/2015 (2015)

    Google Scholar 

  28. Waspmote: Waspmote documentation (1999). http://www.libelium.com/development/waspmote/documentation/gases-board-technical-guide/. Accessed on 16 May 2015

  29. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. The Morgan Kaufmann Series in Data Management Systems, San Francisco (2011)

    Google Scholar 

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Acknowledgments

The authors of the present paper would like to thank the following institutions for their support to develop this work: Consejo Nacional de Ciencia y Tecnología (CONACyT), SNI, Instituto Politécnico Nacional (COFAA, SIP, CIDETEC, CIC, AND ESIME).

Author information

Authors and Affiliations

  1. Centro de Innovación y Desarrollo Tecnológico en Cómputo, CIDETEC, Instituto Politécnico Nacional, IPN, México City, Mexico

    Eduardo Solórzano-Alor

  2. Centro de Investigación en Computación, CIC, Instituto Politécnico Nacional, IPN, México City, Mexico

    Amadeo José Argüelles-Cruz

  3. Unidad Profesional Interdisciplinaria de Ingeniería y Ciencias Sociales y Administrativas, UPIICSA, México City, Mexico

    María Isabel Cajero-Lázaro

  4. Escuela Superior de Ingeniería Mecánica y Eléctrica, ESIME, Instituto Politécnico Nacional, IPN, México City, Mexico

    Miguel Sánchez-Meraz

Authors
  1. Eduardo Solórzano-Alor
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  2. Amadeo José Argüelles-Cruz
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  3. María Isabel Cajero-Lázaro
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  4. Miguel Sánchez-Meraz
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Corresponding author

Correspondence to Eduardo Solórzano-Alor .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

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Solórzano-Alor, E., Argüelles-Cruz, A.J., Cajero-Lázaro, M.I., Sánchez-Meraz, M. (2015). An Embedded Application System for Data Collection of Atmospheric Pollutants with a Classification Approach. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_46

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  • DOI: https://doi.org/10.1007/978-3-319-27060-9_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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