pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter
This work provides a low-cost alternative for air quality monitoring, which could benefit communities and policymakers, but it is incremental as it applies existing methods like LSTM-RNN to a new sensor network.
The authors tackled the problem of high-cost air quality monitoring by developing pmSensing, a low-cost participatory sensing network using IoT devices to measure particulate matter, which showed results close to data from expensive stations and achieved high accuracy in predictions using LSTM-RNN.
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.