pmSensing: Uma Rede de Sensoriamento Participativo para Monitoramento Preditivo de Material Particulado
Abstract
This work presents a proposal for a wireless sensor network for participatory sensing, with IoT sensing devices developed especially for monitoring and predicting air quality, as alternatives of high cost meteorological stations. The system, called pmSensing, aims to measure particulate material. A validation is done by comparing the data collected by the prototype with data from stations. The comparison shows that the results are close, which can enable low-cost solutions to the problem. The system still presents a predictive analysis using recurrent neural networks, in this case the LSTM-RNN network, where the predictions presented high accuracy in relation to the real data.
References
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Pope III, C. A., Dockery, D. W., Spengler, J. D., and Raizenne, M. E. (1991). Respiratory health and pm10 pollution: a daily time series analysis. American Review of Respiratory Disease, 144(3 pt 1):668–674.
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Thaweephol, K. and Wiwatwattana, N. (2019). Long short-term memory deep neural network model for pm2. 5 forecasting in the bangkok urban area. In 2019 17th International Conference on ICT and Knowledge Engineering, pages 1–6. IEEE.
WHO, W. H. O. (2016). Ambient air pollution: A global assessment of exposure and burden of disease.
Abbey, D. E., Nishino, N., McDonnell, W. F., Burchette, R. J., Knutsen, S. F., Lawrence Beeson, W., and Yang, J. X. (1999). Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. American journal of respiratory and critical care medicine, 159(2):373–382.
Al-Ali, A., Zualkernan, I., and Aloul, F. (2010). A mobile gprs-sensors array for air pollution monitoring. IEEE Sensors Journal, 10(10):1666–1671.
Azzouni, A. and Pujolle, G. (2017). A long short-term memory recurrent neural network framework for network traffic matrix prediction. arXiv preprint arXiv:1705.05690.
Boné, R., Assaad, M., and Crucianu, M. (2003). Boosting recurrent neural networks for time series prediction. In Artificial neural nets and genetic algorithms, pages 18–22. Springer.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Crone, S. F. and Kourentzes, N. (2010). Feature selection for time series prediction–a combined filter and wrapper approach for neural networks. Neurocomputing, 73(1012):1923–1936.
da Lima, A. L. and da Silva, V. L. (2015). Micro sensor para monitoramento da qualidade do ar. In Gestão, Tecnologia Industrial e Modelagem Computacional, volume 1.
Dockery, D. W., Pope, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris Jr, B. G., and Speizer, F. E. (1993). An association between air pollution and mortality in six us cities. New England journal of medicine, 329(24):1753–1759.
dos Santos, A. S., Faccini, L. G., Marte, C. L., and Cintra, J. P. (2018). Emprego de componentes móveis de iot para análises espaçotemporais de alta resolução para avaliar a qualidade do ar em cidades médias brasileiras. In Anais do II Workshop de Computação Urbana (COURB 2018), volume 2. SBC.
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.
Hu, J., Wang, X., Zhang, Y., Zhang, D., Zhang, M., and Xue, J. (2020). Time series prediction method based on variant lstm recurrent neural network. Neural Processing Letters, pages 1–16.
IEMA (2021). Qualidade do ar. https://iema.es.gov.br/qualidadedoar.
Karevan, Z. and Suykens, J. A. (2020). Transductive lstm for time-series prediction: An application to weather forecasting. Neural Networks, 125:1–9.
Li, S., Xie, G., Ren, J., Guo, L., Yang, Y., and Xu, X. (2020). Urban pm2. 5 concentration prediction via attention-based cnn–lstm. Applied Sciences, 10(6):1953.
Li, Y., Zhu, Z., Kong, D., Han, H., and Zhao, Y. (2019). Ea-lstm: Evolutionary attentionbased lstm for time series prediction. Knowledge-Based Systems, 181:104785.
Liu, X., Li, B., Jiang, A., Qi, S., Xiang, C., and Xu, N. (2015). A bicycle-borne sensor for monitoring air pollution near roadways. In 2015 IEEE International Conference on Consumer Electronics-Taiwan, pages 166–167. IEEE.
MathWorks (2017). Thingspeak communication library for arduino, esp8266 and esp32.
Pope III, C. A., Dockery, D. W., Spengler, J. D., and Raizenne, M. E. (1991). Respiratory health and pm10 pollution: a daily time series analysis. American Review of Respiratory Disease, 144(3 pt 1):668–674.
Roemer, W., Hoek, G., Brunekreef, B., et al. (1993). Effect of ambient winter air pollution on respiratory health of children with chronic respiratory symptoms. American Journal of Respiratory and Critical Care Medicine, 147(1):118–124.
Roy, S., Bose, R., and Sarddar, D. (2017). Smart and healthy city protecting from carcinogenic pollutants. Int. J. Appl. Environ. Sci, 12:1661–1692.
Thaweephol, K. and Wiwatwattana, N. (2019). Long short-term memory deep neural network model for pm2. 5 forecasting in the bangkok urban area. In 2019 17th International Conference on ICT and Knowledge Engineering, pages 1–6. IEEE.
WHO, W. H. O. (2016). Ambient air pollution: A global assessment of exposure and burden of disease.
Published
2021-08-16
How to Cite
SACHETTI, Lucas L. S.; CUSSUOL, Enzo B.; NOGUEIRA, José Marcos S.; MOTA, Vinicius F. S..
pmSensing: Uma Rede de Sensoriamento Participativo para Monitoramento Preditivo de Material Particulado. In: URBAN COMPUTING WORKSHOP (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 168-181.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17112.
