Uncertainty-Guided Counterfactual Sensor Selection and Safety-Gated Recognition for Autonomous Sensing Agents in IoT Systems

Abstract

Autonomous IoT systems rely on distributed sensing, but sensor faults and communication loss can leave multichannel evidence incomplete or unreliable. Their agents must therefore classify measurements, assess evidence sufficiency, select a sensor to reacquire, and hand off unsafe decisions. We formulate counterfactual sensing: a degraded window defines the factual state, each candidate restoration defines a controlled alternative, and a pre-request utility model estimates loss reduction. Physics-Guided Invariant-Kinematic Fusion (PGIK-Fusion) constructs class evidence, while calibrated risk and cost-sensitive utility select acceptance, sensor request, or handoff from factual information. On the Logistic Activity Recognition Challenge dataset, PGIK-Fusion achieves 0.768 accuracy and a macro-averaged F1 score (Macro-F1) of 0.696, exceeding extreme gradient boosting (XGBoost)--one-dimensional convolutional neural network (1D-CNN) fusion by 2.49 and 2.00 percentage points, respectively. Across seven predefined degradation conditions on held-out subjects, the policy reduces unsafe acceptance from 33.0% to 13.8%; relative to an action-rate-matched random policy, it raises automation accuracy by 18.5 percentage points and lowers expected cost by 24.7%. In the auxiliary within-event wind-turbine study, PGIK-Fusion reaches 0.975 Macro-F1, improves on XGBoost--1D-CNN by 1.17 percentage points, and reduces unsafe acceptance from 20.1% to 8.0%. The evaluation characterizes the controlled responses of a frozen recognition model to specified sensor restorations under predefined fault families and severities. Within this scope, the results demonstrate that calibrated recognition, targeted evidence acquisition, and conservative deferral can form a unified perception--action loop that reduces unsafe automation under sensor degradation.