AI 中文总结
OpenCSI是面向异构网状无线传感器网络的自校准层，通过在线学习空房间基线实现WiFi CSI感知模型零样本跨环境迁移，在二元占用任务上F1最高达0.99，无需目标域数据或重训练。
AI 中文摘要
在一种环境中训练的WiFi CSI感知模型通常无法在另一种环境中使用，因为标准的会话内归一化会将芯片和房间特有的伪影固化到特征空间中，需要为每个新房间或无线电进行重新校准。我们提出OpenCSI，这是一个抽象层，它通过将每个网状链路表示为相对于自身静默时段时间标准差的单一无量纲Z分数来隐藏这些伪影。分母通过短时间空房间自举在线学习，并附带成熟度标签，使下游逻辑能够检测漂移并在基线不可靠时弃权（不执行）。我们在三个不同房间和三代ESP32（S3、C3、C6，涵盖802.11n HT20和802.11ax HE20）的二元占用任务上评估OpenCSI，包括同房间芯片交换实验以分离硬件与几何因素。在一个部署上训练的模型，零样本在几乎所有迁移单元上保持单一空/占用决策阈值，达到二元F1值最高0.99，而标准归一化的F1值降至0.87或完全失效，且无需目标域数据或重新训练。由于时间标准差消除了区分静态与动态运动所需的绝对幅度，该迁移在设计上仅限于二元存在检测。我们发布源代码和数据集以支持可复现的跨环境CSI研究。
英文摘要
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.
