Detection and Localization of Hidden IoT Devices in Unknown Environments Based on Channel Fingerprints
Xu Ju, Yitang Chen, Zhiqiang Li, Biao Han · Big Data and Cognitive Computing · 2025
In recent years, hidden IoT monitoring devices installed indoors have raised significant concerns about privacy breaches and other security threats. To address the challenges of detecting such devices, low positioning accuracy, and lengthy detection times, this paper proposes a hidden device detection and localization system that operates on the Android platform. This technology utilizes the Received Signal Strength Indication (RSSI) signals received by the detection terminal device to achieve the detection, classification, and localization of hidden IoT devices in unfamiliar environments. This technology integrates three key designs: (1) actively capturing the RSSI sequence of hidden devices by sending RTS frames and receiving CTS frames, which is used to generate device channel fingerprints and estimate the distance between hidden devices and detection terminals; (2) training an RSSI-based ranging model using the XGBoost algorithm, followed by multi-point localization for accurate positioning; (3) implementing augmented reality-based visual localization to support handheld detection terminals. This prototype system successfully achieves active data sniffing based on RTS/CTS and terminal localization based on the RSSI-based ranging model, effectively reducing signal acquisition time and improving localization accuracy. Real-world experiments show that the system can detect and locate hidden devices in unfamiliar environments, achieving an accuracy of 98.1% in classifying device types. The time required for detection and localization is approximately one-sixth of existing methods, with system runtime maintained within 5 min. The localization error is 0.77 m, a 48.7% improvement over existing methods with an average error of 1.5 m.