A Hybrid Noise Reduction And Normalization Framework For Improving Multimodal Sensor Data Quality In Real-Time Systems

Multimodal sensor data, integrating signals such as RGB, LiDAR, and IMU, plays a pivotal role in enabling intelligent decision-making in real-time Internet of Things (IoT) systems. However, these data streams are inherently prone to complex noise patterns, cross-sensor inconsistencies, and scaling disparities that conventional preprocessing techniques often fail to address comprehensively. This paper presents a hybrid data preprocessing framework that unifies advanced denoising and adaptive normalization in a single, context-aware pipeline. The framework leverages wavelet-based denoising for high-frequency noise suppression, Kalman filtering for dynamic state estimation, and a real-time adaptive normalization mechanism that calibrates data scaling based on temporal and environmental contexts. Evaluations on synchronized multimodal IoT datasets comprising RGB, LiDAR, and IMU recordings under low-light, high-noise, and adverse-weather conditions (≈ 18,000 aligned samples; 30 Hz, 10 Hz, 100 Hz) show significant performance gains. Results indicate a 30.4% RMSE reduction (p < 0.05), 33% faster convergence, and only 34% computational overhead, while maintaining real-time feasibility with a 41 ms latency per frame. These findings confirm that combining complementary denoising paradigms with adaptive, context-driven normalization enhances signal fidelity and responsiveness in dynamic sensing environments. This contribution presents a reproducible, statistically validated hybrid preprocessing framework for enhancing the quality of multimodal sensor data, enabling more reliable deployments in industrial automation, environmental monitoring, and intelligent transport systems.