pmSensing: Uma Rede de Sensoriamento Participativo para Monitoramento Preditivo de Material Particulado
Resumo
Este trabalho apresenta uma proposta de uma rede de sensores sem fio para sensoriamento participativo, com dispositivos IoT de sensoriamento desenvolvidos especialmente para monitoramento e predição da qualidade do ar, como alternativa a estações meteorológicas de alto custo. O sistema, batizado de pmSensing, objetiva fazer a medição de material particulado. Uma validação é feita comparando os dados coletados pelo protótipo com dados das estações. A comparação mostra que os resultados são próximos, o que pode viabilizar soluções de baixo custo para o problema. O sistema ainda apresenta uma análise preditiva utilizando redes neurais recorrentes, no caso a rede LSTM-RNN, onde as predições apresentaram alta acurácia em relação aos dados reais.
Referências
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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.
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.
Publicado
16/08/2021
Como Citar
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: WORKSHOP DE COMPUTAÇÃO URBANA (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.
