Smart Home Automation: Architecture, Communication Protocols, and Performance Evaluation of an IoT-Based Edge-Assisted System

This paper presented an edge-assisted, five-layer architecture for smart home automation and evaluated it through simulation. The hybrid decision pipeline combining rule-based control with learned occupancy prediction reduced monthly energy consumption by 28.8%, lowered command latency to under 100 ms for local Zigbee and Thread control compared with 820 ms for cloud-routed Wi-Fi, and attained 94.6% occupancy prediction accuracy with an LSTM model. The protocol comparison and security analysis provide practical guidance for system designers. Future work will deploy the system in physical homes, integrate Matter-native devices, and explore federated learning for privacy-preserving personalization.

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