Abstract

The growing deployment of Internet of Things (IoT) monitoring systems has resulted in demands for low-latency, secure, and energy-efficient intelligence on embedded chips. But most cloud-based and edge-assisted solutions are prone to high communication latency, lack adaptability, consume more energy, and lack decision security under resource-limited conditions. This paper introduces an Edge-Intelligent IoT Framework for Real-Time Adaptive Monitoring and Trust-Aware Secure Decision Validation with Resource-Aware Artificial Intelligence and Machine Learning (AI/ML) on embedded chips. Unlike conventional TinyML or Edge AI deployments that use a fixed inference model, the proposed framework introduces a validation-calibrated adaptive inference mechanism that jointly considers chip resources, input complexity, and sensor trust before accepting an embedded decision. The main scientific contribution is the unified coupling of resource-aware model selection with trust-aware decision validation for low-power embedded IoT inference. The framework dynamically selects the inference path and validates sensor trust before decision acceptance. Through experimentation, the proposed framework is demonstrated with 97.2% accuracy, 96.4% F1-score, and 98.1% AUROC, and 40.4% lower inference latency (31.2 ms to 18.6 ms) and 39.6% lower energy (9.6 mJ to 5.8 mJ) compared with traditional TinyML deployment. These results were obtained using the MHEALTH wearable IoT dataset with a leakage-safe 70:15:15 split and were statistically validated across five independent runs. The findings demonstrate a promising resource-aware TinyML-style embedded inference pipeline for wearable IoT monitoring, with improved latency-energy efficiency and trust-aware decision validation under the evaluated settings.

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