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The proliferation of IoT devices has led to the development of smart appliances, gadgets, and instruments to realize a significant vision of a smart home. Conspicuously, this paper presents an intelligent framework of a foot-mat-based intruder-monitoring and detection system for a home-based security system. The presented approach incorporates fog computing technology for analysis of foot pressure, size, and movement in real time to detect personnel identity. The task of prediction is realized by the predictive learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) through which the proposed model can estimate the possibility of an intruder. In addition to this, the presented approach is designed to generate a warning and emergency alert signals for real-time indications. The presented framework is validated in a smart home scenario database, obtained from an online repository comprising 49,695 datasets. Enhanced performance was registered for the proposed framework in comparison to different state-of-the-art prediction models. In particular, the presented model outperformed other models by obtaining efficient values of temporal delay, statistical performance, reliability, and stability.

IoT-Inspired Framework of Intruder Detection for Smart Home Security Systems