OpenCSI: Self-Calibration Layer for Heterogeneous Mesh Wireless Sensor Networks

2026-07-29

AI Summary

This study addresses the significant performance degradation of WiFi CSI-based sensing models when deployed across different environments due to hardware discrepancies and environment-specific artifacts, which typically necessitate frequent recalibration. To overcome this limitation, the authors propose OpenCSI, a self-calibrating abstraction layer that represents mesh-link CSI sequences as dimensionless Z-scores normalized by the temporal standard deviation during silent periods. This approach effectively masks variations arising from both chipsets and room characteristics, enabling zero-shot cross-environment transfer. The work further introduces an online self-calibration mechanism incorporating empty-room guided initialization and maturity-aware labeling to support drift detection and reliability assessment without requiring target-domain data. Experimental results demonstrate that, across three distinct rooms and three generations of ESP32 chips, the method achieves a binary presence detection F1 score of 0.99, substantially outperforming conventional normalization techniques, which either drop to an F1 of 0.87 or fail entirely.

Abstract

WiFi CSI sensing models trained in one environment usually fail in another because standard per-session normalization bakes chip- and room-specific artifacts into feature space, requiring fresh calibration for every new room or radio. We propose OpenCSI, an abstraction layer that hides these artifacts by exposing each mesh link as a single dimensionless Z-score against its own quiet-period temporal standard deviation. The denominator is learned online from a short empty-room bootstrap and reported with a maturity tag, enabling downstream logic to detect drift and abstain when baselines become unreliable. We evaluate OpenCSI on binary occupancy across three distinct rooms and three ESP32 generations (S3, C3, C6, spanning 802.11n HT20 and 802.11ax HE20), including a same-room chip swap isolating hardware from geometry. A model trained on one deployment holds a single empty-versus-occupied decision threshold zero-shot across nearly all transfer cells, reaching binary F1 up to 0.99 where standard normalization drops to 0.87 or fails outright, with no target-domain data or retraining. The transfer is scoped to binary presence by construction, as distinguishing static from moving motion requires the absolute magnitude that temporal standard deviation removes. We release the source code and dataset to support reproducible cross-environment CSI research.

Problem

• WiFi CSI sensing
• cross-environment transfer
• hardware heterogeneity
• self-calibration
• wireless sensor networks

Innovation

• self-calibration
• WiFi CSI sensing
• zero-shot transfer
• heterogeneous mesh networks
• temporal standard deviation normalization