Utilizing Large Language Models for Automated Log-Based Thing Description Generation

Abstract

Purpose: Semantic device descriptions, such as the Web of Things (WoT) Thing Description (TD), are a great tool to improve the level of interoperability in Internet of Things (IoT) systems. However, a majority of new and existing IoT devices do not ship with a TD, and developers often need to create them by hand. This makes it tedious for anyone who wishes to integrate, migrate, or modernize devices of their existing infrastructure into a WoT ecosystem. Therefore, an automated approach for TD generation that facilitates this process is needed.

Methodology: We propose a Large Language Model (LLM)-based approach to automate TD generation. By utilizing message logs and conformance checks, we introduce an iterative process that leverages LLM technologies to generate TDs. The proposed methodology is evaluated in a case study of 76 IoT devices communicating over MQTT.

Findings: Our results show that with the proposed methodology, an LLM can generate TDs from MQTT message logs with an average functional accuracy of up to 91%, and a descriptive accuracy of around 85%, demonstrating strong overall performance.

Value: All generated TDs and the prototypical Python implementation of the methodology can be found in our repository. The proposed methodology helps the adoption of the WoT by offering an automated generation of TDs in environments where MQTT message logs are available.