VAS-DPFF: Virtual Augmented Sensor Based on Deterministic and Probabilistic Feature Fusion for Environmental Monitoring

Smart sensor networks for environmental monitoring require accurate and continuous estimation of key variables such as temperature, humidity, and wind speed; however, physical sensor deployments are frequently limited by high costs, hardware failures, and data quality degradation, while existing virtual sensor approaches rely on single-model architectures that lack explicit uncertainty modeling and fail to capture the complex non-linear dynamics of real-world IoT time-series data. This paper proposes VAS-DPFF, a virtual augmented sensor framework based on deterministic and probabilistic feature fusion, which contributes a principled integration of well-established deterministic and probabilistic techniques within a unified AIoT-compatible virtual sensing architecture. The framework integrates: (i) a deterministic pipeline comprising temporal encoding, rolling statistics, and mutual information-based feature selection; (ii) a probabilistic pipeline employing Bayesian Ridge Regression (BRR) and Gaussian Process Regression (GPR) to generate uncertainty-aware synthetic features; and (iii) an early feature-level fusion strategy feeding an XGBoost regression model augmented with Gaussian noise injection. Experiments on 84,582 time-series records from a nine-station IoT environmental monitoring network in Gwacheon City, South Korea, demonstrate strong multi-target prediction performance: temperature RMSE =0.811 ∘C, R2=0.973; humidity RMSE =4.113%, R2=0.964; and wind speed RMSE =0.602 m/s, R2=0.798, representing RMSE reductions of 61.2%, 60.7%, and 62.3% over the existing method, respectively. Comprehensive ablation studies, sensitivity analysis, and augmentation validation confirm that the proposed integration of deterministic and probabilistic features yields consistent and practically valuable improvements in virtual sensing performance suitable for AIoT-enabled smart sensor network deployments across multiple environmental monitoring targets.