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

arXiv: https://arxiv.org/abs/2111.11441 · HTML: https://mlsanity.com/paper/2111.11441/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction](https://mlsanity.com/paper/1811.03760/) (2018)
- [A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction](https://mlsanity.com/paper/1705.05690/) (2017)

## Abstract (arXiv)

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.