Bayesian Input Compression for Edge Intelligence in Industry 4.0

In Industry 4.0 environments, edge intelligence plays a critical role in enabling real-time analytics and autonomous decision-making by integrating artificial intelligence (AI) with edge computing. However, deploying deep neural networks (DNNs) on resource-constrained edge devices remains challenging due to limited computational capacity and strict latency requirements. While conventional methods primarily focus on structural model compression, we propose an adaptive input-centric approach that reduces computational overhead by pruning redundant features prior to inference. A Bayesian network is employed to quantify the influence of each input feature on the model output, enabling efficient input reduction without modifying the model architecture. A bidirectional chain structure facilitates robust feature ranking, and an automated algorithm optimizes input selection to meet predefined constraints on model accuracy and size. Experimental results demonstrate that the proposed method significantly reduces memory usage and computation cost while maintaining competitive performance, making it highly suitable for real-time edge intelligence in industrial settings.