# ST-GraphRCA: A Root Cause Analysis Model for Spatio-Temporal Graph Propagation in IoT Edge Computing

## TL;DR

ST-GraphRCA is a new model for identifying root causes of anomalies in IoT edge computing systems using spatio-temporal graphs.

## Contribution

Proposes ST-GraphRCA, a novel root cause analysis model for spatio-temporal graph propagation in IoT edge computing.

## Key findings

- ST-GraphRCA achieves an F1-Score of 0.89 in root cause analysis.
- The model reduces localization time to 238.8 ms in resource-constrained edge scenarios.
- Outperforms existing methods in fault tracing for large-scale IoT systems.

## Abstract

Real-time processing demands for massive IoT sensor data necessitate reliance on distributed microservice systems within edge clusters. However, pinpointing the root cause of anomalies within these edge microservice clusters poses a critical challenge for intelligent IoT operation and maintenance. To address the issue, a spatio-temporal graph propagation model ST-GraphRCA is proposed for root cause analysis in IoT edge environments. Our approach begins by resolving the fundamental issue of time-series asynchrony across distributed multi-source metrics. A PCA-DTW hybrid feature extraction method is introduced with a dynamic alignment strategy to mitigate the effects of random network delays and data deformation without requiring prior synchronization. Subsequently, ST-GraphRCA constructs a stream-based forward propagation graph based on the flow conservation principle. By integrating dynamic edge weights with node-level input–output anomaly scores, ST-GraphRCA precisely infers fault propagation pathways and identifies potential root cause candidates through causal reasoning. Finally, a topology-constrained high-utility mining algorithm filters these candidates. Using a constraint matrix, the algorithm filters out unreachable service combinations to locate low-frequency and high-risk root causes. Experimental results indicate that ST-GraphRCA achieves an F1-Score of 0.89, outperforming existing methods. In resource-constrained edge scenarios, its average localization time is merely 238.8 ms, representing a six-fold improvement over key benchmarks. Thus, ST-GraphRCA not only provides an efficient anomaly fault tracing solution for large-scale IoT systems but also offers technical support for the intelligent operation and maintenance of distributed microservice systems.