Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models
Researchers show CSI-trained Wi-Fi models can localize people from RSSI data at ~80% confidence, enabling privacy attacks from ordinary IoT devices.
The paper investigates cross-domain inference, feeding RSSI data into an existing CSI-based Wi-Fi pose prediction model. RSSI is accessible on IoT devices without elevated OS permissions or specialized drivers, unlike CSI. Using an RSSI dataset synchronized with video ground truth, the model predicted human locations with approximately 80% confidence when movement was present. The results imply a wide range of commodity IoT devices could be used for privacy invasion in Wi-Fi-dense environments.
- RSSI requires no elevated permissions, broadening the range of attack-capable IoT devices
- CSI-trained model accepts low-granularity RSSI input via cross-domain inference
- Localizes moving humans at roughly 80% confidence
- Demonstrates privacy risk in Wi-Fi-dense environments
From the original article
1 min read at arxiv.orgWi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collecting this data on typical IoT devices often requires elevated operating system permissions and specialized drivers. Consequently, this paper investigates the feasibility of utilizing Received Signal Strength Indicator (RSSI) data to predict human locations.
By Ariel Duschanek-Myers · arxiv.org
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