LLM-Driven Context-Aware Health Monitoring for Resource-Constrained Edge Devices
TL;DR
An LLM-driven, context-aware framework that integrates real-time system metrics, historical data, and task-specific importance levels for anomaly detection and prediction is proposed, enabling proactive intervention before critical operating conditions are reached.
Abstract
Resource-constrained edge devices require efficient and adaptive health monitoring to ensure reliable operation under dynamic workloads. This paper proposes an LLM-driven, context-aware framework that integrates real-time system metrics, historical data, and task-specific importance levels for anomaly detection and prediction. Specifically, the framework forecasts the semantic health state of the edge device five minutes ahead based on recent monitoring observations, enabling proactive intervention before critical operating conditions are reached. Multidimensional metrics, including CPU, memory, temperature, network load, and process information, are transformed into structured time-series representations and used as input to pre-trained machine learning models. A large language model (LLM) acts as an orchestration layer, dynamically selecting the most appropriate predictive model based on system context and resource constraints. This enables adaptive trade-offs between accuracy, latency, and computational cost. Experimental results on Raspberry Pi devices show that the proposed approach achieves comparable or improved performance while reducing resource usage compared to static methods.