Real-time PM10 Concentration Prediction LSTM Model based on IoT Streaming Sensor data

IoT 스트리밍 센서 데이터에 기반한 실시간 PM10 농도 예측 LSTM 모델

  • Kim, Sam-Keun ;
  • Oh, Tack-Il
  • 김삼근 ;
  • 오택일
  • Received : 2018.09.28
  • Accepted : 2018.11.02
  • Published : 2018.11.30


Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.


Long Short Term Memory;PM10;Prediction model;Recurrent Neural Network;Sequence data


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