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Sensors, Vol. 18, Pages 1711: Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

Sensors, Vol. 18, Pages 1711: Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

Sensors doi: 10.3390/s18061711

Authors: Nida Saddaf Khan Sayeed Ghani Sajjad Haider

IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.

Authors:   Khan, Nida Saddaf; Ghani, Sayeed ; Haider, Sajjad
Journal:   Sensors
Volume:   18
edition:   6
Year:   2018
Pages:   1711
DOI:   10.3390/s18061711
Publication date:   25-May-2018
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