A Drift-Aware Human-in-the-Loop Edge AI Agent for Wireless IoMT Sensor-Network Intrusion Detection Under Cross-Corpus Shift

Wireless sensor networks underpin the Internet of Medical Things (IoMT), where connected medical devices and wearable body-area sensors stream patient telemetry across hospital networks, and securing this traffic is safety-critical. Machine learning intrusion-detection systems for the IoMT are usually evaluated within a single public dataset, where they report near-perfect detection scores; whether that accuracy predicts performance in a different hospital has not been measured systematically. We frame the deployed detector as a lightweight human-in-the-loop edge AI agent that observes local traffic, asks an analyst to label a small set of representative flows, trains a tiny model on-premises, and monitors the traffic distribution for drift. Using cross-corpus shift across four public datasets, comprising three sensor-network corpora (WUSTL-EHMS-2020, CIC-IoMT-2024, TON-IoT) and an unrelated enterprise-network corpus, as a reproducible stand-in for cross-hospital deployment, we find the shift is near its theoretical maximum on all twelve transfer directions, that unlabeled domain-adaptation methods collapse on the hardest medical-telemetry targets, and that external pretraining adds no measurable benefit once training schedules are matched, consistent with a domain-adaptation error bound that the measured divergence renders vacuous. The same 1206-parameter network trained from scratch on ten to fifty locally labeled flows matches or exceeds every transfer alternative across all four corpora; on the six sensor-network transfer directions it also outperforms larger and classical local models. A closed-loop evaluation on simulated drifting streams shows the calibrated trigger detects drift within two 500-flow windows at under one false alarm per hundred stationary windows, retraining restores accuracy, and the agent tolerates ten percent analyst error for about five F1 points; we also map the detector’s exposure to white-box evasion and targeted label poisoning.