Constant video data streaming to central servers’ costs high bandwidth and storage. This article proposes a lightweight and cost-effective secure smart home infrastructure employing a single board computer and a software motion for camera surveillance. Video screens from several cameras are monitored by the motion program, which acts when movement is detected. The presented framework also sends email and smartphone messages to the smart homeowner efficiently if motion is detected. To increase the sustainability of the framework above and beyond, we have integrated renewable energy to power the NVIDIA Jetson Nano and the cameras as opposed to conventional sources of energy, making our framework eco-friendly. Four advanced technology and alert notification methodologies are compared. For both indoor and outdoor environments, the effectiveness and adaptability of the Edge AI (AI on edge) powered IoT framework for smart surveillance has been evaluated. The framework could achieve 94% accuracy, 92% precision, and 96% recall in indoor scenarios and showed its ability to detect motion in challenging indoor scenarios. Despite difficulties like weather, foliage, and animal disturbances, it remained accurate to 87%, precise at 85%, and recalled 92% in outdoor areas.
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This work was supported by Dongseo University, "Dongseo Frontier Project" Research Fund of 2023.
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The Dongseo Frontier Project Research Fund of 2023.
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Hee Woong Jeong
Department of Architectural Design, Dong-Seo University, Sasang-gu, Busan, Republic of Korea.
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Cite this article
Hee Woong Jeong, “A Cost-Effective Solution for Automation, Security and Energy Efficiency in Edge Enabled IoT Smart Home Applications”, Journal of Machine and Computing, pp. 847-856, April 2025, doi: 10.53759/7669/jmc202505066.