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ARTICLE
A COMPREHENSIVE FRAMEWORK FOR SECURE AND PRIVATE SMART HOME INFRASTRUCTURE USING DECENTRALIZED EDGE AI
Issue Vol. 1 No. 01 (2024): Volume 01 Issue 01 --- Section Articles
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Abstract
The proliferation of Internet of Things (IoT) devices has revolutionized home automation, yet it has simultaneously introduced significant security and privacy vulnerabilities. Traditional smart home security systems often rely on centralized, cloud-based processing, leading to critical challenges such as high latency in alert systems, substantial bandwidth consumption, and an increased risk of sensitive data breaches. This paper proposes a comprehensive, decentralized framework that leverages Edge Artificial Intelligence (Edge AI) to create a more secure, responsive, and private smart home infrastructure. By performing sophisticated, AI-driven data processing—including multi-stage motion analysis, object classification, and threat verification—directly on local edge devices, the proposed system fundamentally minimizes the transmission of raw, private data to the cloud. This edge-centric architecture not only enhances user privacy by design but also ensures real-time threat detection and response with minimal delay, maintaining operational integrity even during internet outages.
The framework integrates a network of IoT sensors and high-definition cameras managed by a local edge hub (e.g., a single-board computer) that runs a pipeline of lightweight, optimized AI models for intelligent, autonomous surveillance. We detail a multi-layered architecture encompassing an Intelligent Sensing Layer, an Edge Processing and AI Inference Layer, a Secure Communication Layer, and a Cloud Interaction Layer. A key contribution is an advanced AI pipeline at the edge that uses initial motion filtering to trigger a more sophisticated object detection model, effectively reducing the false positives common in traditional systems (e.g., from pets, insects, or environmental changes). We present a detailed implementation and a rigorous experimental evaluation conducted over 30 days in both indoor and outdoor scenarios. The results demonstrate the framework's superior performance, achieving high accuracy (91% indoor, 85% outdoor) and significantly lower notification latency compared to existing methodologies. This work validates the efficacy of an edge-based approach and provides a detailed blueprint for developing robust, autonomous, and trustworthy security solutions that address the critical limitations of conventional cloud-centric models.
Keywords
References
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