Interference Sensing-as-a-Service: End–Edge–Cloud IoT System With Zero-Shot Detection and GNN Localization

Abstract

Urban Internet of Thing (IoT) networks face severe reliability threats from diverse wireless interference, including jamming and spoofing, which are difficult to detect and localize in multipath-rich environments. Existing schemes often suffer from high false alarms, poor generalization, and low localization accuracy. This article presents an end–edge–cloud interference detection and localization framework integrating zero-shot detection, game-theoretic collaborative sensing, and GNN-based localization. Experiments on a city-scale prototype show that the system achieves 98.5% detection accuracy with recall of 96.7%, while reducing false alarms to 3.2%. The proposed graph neural network (GNN) reduces median localization error to 12.3 m, significantly outperforming baseline methods. Furthermore, the architecture reduces energy consumption by nearly 40% compared with cloud-only designs and maintains end-to-end latency under 120 ms. These results demonstrate that the proposed system enables robust, real-time interference awareness for large-scale IoT deployments, paving the way toward resilient 6G smart cities.