HomeNetEdge: A Lightweight Edge-AI Smart Home Framework with Secure Blockchain Integration and Multi-Model Predictive Learning

Abstract

The proliferation of the Internet of Everything (IoE) has transformed smart home environments, necessitating intelligent, secure, and responsive systems that can make real-time decisions at the network edge. This paper presents HomeNetEdge, an edge-AI-enabled smart home automation framework that combines lightweight ensemble learning models with blockchain-based security to address the pressing challenges of privacy, identity theft, and autonomous device control. The system integrates logistic regression and random forest classifiers using principal component analysis (PCA) for feature selection, ensuring accurate and efficient predictions of device states (“ON”/“OFF”) with minimal computational overhead. HomeNetEdge operates on commercial off-the-shelf hardware, including Raspberry Pi servers, 5V relay modules, and sensor arrays, and leverages local edge computing to eliminate reliance on cloud infrastructure. A custom Android application interfaces with the system via HTTP and Apache servers for seamless user control. To ensure robust security and trustworthy communication between devices, blockchain-based authentication mechanisms are embedded. The framework is evaluated under both controlled and real-life conditions, demonstrating real-time performance, scalability, and resilience against cyber threats. This work contributes a practical, cost-effective, and scalable edge computing architecture that supports real-time AI-driven decision-making, personalized automation, and secure data handling – critical for the future of intelligent IoT ecosystems.