An Energy‐Efficient Intelligent Edge Surveillance Expert System for Real‐Time Intrusion Detection

This article presents an energy‐efficient intelligent edge surveillance expert system for real‐time intrusion detection in resource‐constrained environments. Unlike conventional continuous surveillance systems, the proposed framework introduces a motion‐triggered inference mechanism using passive infrared (PIR) sensing, enabling selective activation of deep learning models. This transforms surveillance into an event‐driven expert system that integrates perception, reasoning, and decision‐making. The system achieves 95.54% accuracy and 93.07% F1‐score, and reduces energy consumption by ~40% compared to continuous inference. The proposed architecture demonstrates improved detection reliability, reduced false alarms, and enhanced computational efficiency, making it suitable for scalable embedded surveillance applications.