2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 3 , Issue 2 , PP: 85-94, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud Ismail 1 * , Shereen Zaki 2 , Heba Rashad 3
Doi: https://doi.org/10.54216/JISIoT.030204
Energy management in smart homes involves the use of technology to optimize energy consumption, reduce waste, and lower energy costs. Smart homes are equipped with various devices, sensors, and systems that are designed to monitor and control energy usage. We proposed a novel Energy Management System (EMS) that integrates Machine Learning (ML) techniques and IoT paradigms to optimize energy consumption and reduce energy costs for sustainable smart homes. In addition to the AI-based EMS, we propose integrating fog computing, a decentralized computing infrastructure, to improve the speed, accuracy, privacy, and security of the EMS. The fog nodes can collect data from the various sensors and devices in the smart home and process the data in real time, reducing latency and allowing for quicker decision-making. By processing data at the edge of the network, fog computing also reduces the amount of data that needs to be sent to the cloud, improving privacy and security. Experimental proof-of-concept simulations demonstrated the efficiency and effectiveness of our system in improving sustainability in smart homes.
Smart Homes , Sustainability , Intelligent Energy Management , Fog Computing
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