SIS-NET: Sensor Identity-Aware Set Modeling for Sensor-Configuration-Agnostic Electronic Nose Recognition

Electronic-nose recognition models are commonly trained with a fixed sensor array and therefore implicitly depend on a fixed number, identity, and ordering of input sensors. This assumption is fragile in deployment, where sensors may be unavailable because of hardware variants, maintenance, or externally identified unreliable measurements. We study whether a single model can recognize samples under different subsets of a known sensor universe without configuration-specific retraining. We propose SIS-NET, a sensor-set model that applies a shared temporal encoder to each available sensor, incorporates an explicit sensor identity embedding, and aggregates the resulting variable-size token set with masked mean pooling. During training, sensor subsets are sampled from multiple availability levels so that the model experiences configuration variation before inference. We evaluate the same checkpoint under full, fixed-subset, random-subset, and held-out-combination conditions. The experiments are designed to distinguish configuration-agnostic recognition from fixed-input zero filling, electronic-nose fault-tolerant ensembles, and multivariate time-series baselines.