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.
翻译:这项工作提出了建立参与性遥感无线传感器网络的建议,开发用于监测和预测空气质量的IoT遥感装置,作为高成本气象台站的替代物;该系统称为PmSensing,旨在测量颗粒物质;通过比较原型收集的数据和来自各台站的数据进行验证;比较表明,结果接近,能够以低成本解决问题;该系统仍然利用经常性神经网络,即LSTM-RNN,提供预测性分析,预测显示与真实数据相比的准确性很高。