A Root Cause Analysis Framework for IoT Based on Dynamic Causal Graphs Assisted by LLMs

The identification of the root causes of failures in complex Internet of Things (IoT) systems has always presented a significant challenge. Despite the extensive range of algorithms and technologies already available for root cause analysis (RCA) in various fields, there remains a lack of RCA methods specifically designed for IoT systems. The present paper proposes an IoT system RCA framework based on dynamic causal graphs, assisted by large language models (LLMs), called LLMs-DCGRCA. First, in response to the issues with traditional causal hypothesis methods, which rely on human experience and suffer from key variable omission and incorrect causal direction assumptions, this article proposes a method for generating causal hypotheses for IoT systems by using knowledge graphs to enhance the performance of LLMs. Second, in response to the challenge that traditional causal learning methods in IoT scenarios struggle to capture causal relationships across the temporal dimension, this article proposes a dynamic causal graph learning method that incorporates causal constraints. Finally, in response to the limitations of traditional RCA methods in IoT scenarios in accurately capturing the dynamic characteristics of anomaly propagation, this article proposes a cumulative root cause localization method based on dynamic causal graphs. The evaluation of LLMs-DCGRCA is conducted using IoT trace data collected from simulation environments and GAIA, a widely used public dataset in the field of intelligent operations and maintenance. The evaluation results demonstrate that LLMs-DCGRCA achieves average HR@7 improvements of 14.04% and 9.35% compared to baseline methods on the two datasets, respectively.