4.6 Article

Towards Smart Home Automation Using IoT-Enabled Edge-Computing Paradigm

Journal

SENSORS
Volume 21, Issue 14, Pages -

Publisher

MDPI
DOI: 10.3390/s21144932

Keywords

smart home; home automation; cloud computing; edge computing; raspberry pi; internet of things

Funding

  1. Department of Computer Science (IDI), Norwegian University of Science and Technology, NTNU in Gjovik, Norway

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Smart home applications have become popular due to IoT technology, making homes more convenient, efficient, and secure. Our research proposes a cost-effective solution for smart home automation using Raspberry Pi, allowing remote and automatic control of home appliances while ensuring customer privacy.
Smart home applications are ubiquitous and have gained popularity due to the overwhelming use of Internet of Things (IoT)-based technology. The revolution in technologies has made homes more convenient, efficient, and even more secure. The need for advancement in smart home technology is necessary due to the scarcity of intelligent home applications that cater to several aspects of the home simultaneously, i.e., automation, security, safety, and reducing energy consumption using less bandwidth, computation, and cost. Our research work provides a solution to these problems by deploying a smart home automation system with the applications mentioned above over a resource-constrained Raspberry Pi (RPI) device. The RPI is used as a central controlling unit, which provides a cost-effective platform for interconnecting a variety of devices and various sensors in a home via the Internet. We propose a cost-effective integrated system for smart home based on IoT and Edge-Computing paradigm. The proposed system provides remote and automatic control to home appliances, ensuring security and safety. Additionally, the proposed solution uses the edge-computing paradigm to store sensitive data in a local cloud to preserve the customer's privacy. Moreover, visual and scalar sensor-generated data are processed and held over edge device (RPI) to reduce bandwidth, computation, and storage cost. In the comparison with state-of-the-art solutions, the proposed system is 5% faster in detecting motion, and 5 ms and 4 ms in switching relay on and off, respectively. It is also 6% more efficient than the existing solutions with respect to energy consumption.

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