OpenCSI: Self-Calibration Layer for Heterogeneous Mesh Wireless Sensor Networks
Journal
2026 IEEE 51st Conference on Local Computer Networks (LCN)
ISSN-Digital
2832-1421
Type
conference paper
Date Issued
2026-09-07
Author(s)
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.
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
IEEE
Event Title
IEEE 51st Conference on Local Computer Networks (LCN)
Event Location
Coimbra, Portugal
Event Date
07-09 October 2026
Subject(s)
Division(s)