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Open AccessArticle

Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

1
Telecommunication Research Lab, Department of Computer Science, Institute of Business Administration, Garden/Kayani Shaheed Road, Karachi 74400, Pakistan
2
Artificial Intelligence Lab, Department of Computer Science, Institute of Business Administration, Garden/Kayani Shaheed Road, Karachi 74400, Pakistan
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(6), 1711; https://doi.org/10.3390/s18061711
Received: 18 April 2018 / Revised: 16 May 2018 / Accepted: 19 May 2018 / Published: 25 May 2018
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. View Full-Text
Keywords: sensor analytics; flowmeter; internet of things (IoT); real-time data; Artificial Neural Network (ANN); MSA forecasting sensor analytics; flowmeter; internet of things (IoT); real-time data; Artificial Neural Network (ANN); MSA forecasting
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MDPI and ACS Style

Khan, N.S.; Ghani, S.; Haider, S. Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home. Sensors 2018, 18, 1711. https://doi.org/10.3390/s18061711

AMA Style

Khan NS, Ghani S, Haider S. Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home. Sensors. 2018; 18(6):1711. https://doi.org/10.3390/s18061711

Chicago/Turabian Style

Khan, Nida S.; Ghani, Sayeed; Haider, Sajjad. 2018. "Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home" Sensors 18, no. 6: 1711. https://doi.org/10.3390/s18061711

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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