pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter

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, where the predictions presented high accuracy in relation to the real data.

[1]  Bin Li,et al.  A bicycle-borne sensor for monitoring air pollution near roadways , 2015, 2015 IEEE International Conference on Consumer Electronics - Taiwan.

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Jinchang Ren,et al.  Urban PM2.5 Concentration Prediction via Attention-Based CNN–LSTM , 2020, Applied Sciences.

[5]  Nikolaos Kourentzes,et al.  Feature selection for time series prediction - A combined filter and wrapper approach for neural networks , 2010, Neurocomputing.

[6]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[7]  J D Spengler,et al.  Respiratory health and PM10 pollution. A daily time series analysis. , 1991, The American review of respiratory disease.

[8]  A R Al-Ali,et al.  A Mobile GPRS-Sensors Array for Air Pollution Monitoring , 2010, IEEE Sensors Journal.

[9]  Jianru Xue,et al.  Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network , 2020, Neural Processing Letters.

[10]  B. Brunekreef,et al.  Effect of ambient winter air pollution on respiratory health of children with chronic respiratory symptoms. , 1993, The American review of respiratory disease.

[11]  Zahra Karevan,et al.  Transductive LSTM for time-series prediction: An application to weather forecasting , 2020, Neural Networks.

[12]  Michel Crucianu,et al.  Boosting Recurrent Neural Networks for Time Series Prediction , 2003, ICANNGA.

[13]  Guy Pujolle,et al.  A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction , 2017, ArXiv.

[14]  W. Mcdonnell,et al.  Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. , 1999, American journal of respiratory and critical care medicine.

[15]  Yao Zhao,et al.  EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction , 2018, Knowl. Based Syst..

[16]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.