A Deep Learning Approach for Root Cause Analysis in Real-Time IIoT Edge Networks
Ahsan Saleem, Mahsa Raeiszadeh, Amin Ebrahimzadeh, Roch Glitho, Johan Eker, Raquel A. F. Mini · 2023
The Industrial Internet of Things (IIoT) applications is usually associated with stringent latency requirements. An anomaly in an IIoT edge network deteriorates the performance and thus needs a real-time Root Cause Analysis (RCA) to identify the anomalous node and provide robust network infrastructure. In this paper, we present an automated, real-time RCA technique to identify the network-level root cause nodes. We use a deep learning-based approach, exploiting a Graph Neural Network (GNN) to identify root cause nodes. In GNN-RCA, we used a sampling technique and optimized aggregator function to reduce detection time. We have shown that the proposed GNNRCA method outperforms the existing benchmarks in terms of classification score and execution time.