Big Data and Personalisation for Non-Intrusive Smart Home Automation

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors h...

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Bibliographic Details
Published in:Big data and cognitive computing 2021-03, Vol.5 (1), p.6
Main Authors: Asaithambi, Suriya, Venkatraman, Sitalakshmi, Venkatraman, Ramanathan
Format: Article
Language:English
Subjects:
Appliances
Automation
Big Data
Customization
Data analysis
Data collection
Data transmission
Decision making
Digital media
Electronic devices
home automation
Home environment
Internet of Things
IoT
Machine learning
Privacy
Sensors
Smart buildings
smart home
Smart houses
Wireless access points
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Summary:With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc5010006