Abstract
The rapid expansion of Internet of Things (IoT) systems has transformed industries through real-time monitoring and automation, generating vast and heterogeneous data streams. As IoT networks expand, the increasing volume and diversity of data, spanning real-time telemetry, device logs, and historical records, complicate the management of IoT systems, including system monitoring, analysis, and reasoning. To address this challenge, we introduce LUMEN (Large Language Models as Unified Multi-Agent Systems for IoT ENhancement), a novel approach combining multi-agent Large Language Models (LLMs), knowledge graphs, and heterogeneous databases to enable cognitive digital twins for IoT observability. LUMEN models IoT systems as knowledge graphs, capturing device relationships and metadata while monitoring data is stored in time-series or object databases. Specialized LLM-based agents collaborate dynamically to analyze IoT systems and explain the findings in natural language, generating and executing analysis code when necessary. Integrated with off-the-shelf network monitoring tools, LUMEN facilitates semantic reasoning and human-in-the-loop collaboration, delivering adaptive insights across diverse data contexts. Two industrial case studies demonstrate the ability of LUMEN to automate analysis workflows, enhance system adaptability, and provide interpretable analytics. This work advances IoT observability by integrating LLMs, semantic intelligence, and explainable analytics into a scalable and adaptive solution using a multi-agent architecture for complex IoT systems.