AutoIoT: Automated IoT Platform Using Large Language Models

• URL: http://arxiv.org/abs/2411.10665v1
• Date: Sat, 16 Nov 2024 02:02:01 GMT
• Title: AutoIoT: Automated IoT Platform Using Large Language Models
• Authors: Ye Cheng, Minghui Xu, Yue Zhang, Kun Li, Ruoxi Wang, Lian Yang,
• Abstract summary: AutoIoT is an automated IoT platform based on Large Language Models (LLMs) and formal verification techniques. We show how AutoIoT can help users generate conflict-free automation rules and assist developers in generating codes for conflict detection.
• Score: 14.481067833984474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: IoT platforms, particularly smart home platforms providing significant convenience to people's lives such as Apple HomeKit and Samsung SmartThings, allow users to create automation rules through trigger-action programming. However, some users may lack the necessary knowledge to formulate automation rules, thus preventing them from fully benefiting from the conveniences offered by smart home technology. To address this, smart home platforms provide pre-defined automation policies based on the smart home devices registered by the user. Nevertheless, these policies, being pre-generated and relatively simple, fail to adequately cover the diverse needs of users. Furthermore, conflicts may arise between automation rules, and integrating conflict detection into the IoT platform increases the burden on developers. In this paper, we propose AutoIoT, an automated IoT platform based on Large Language Models (LLMs) and formal verification techniques, designed to achieve end-to-end automation through device information extraction, LLM-based rule generation, conflict detection, and avoidance. AutoIoT can help users generate conflict-free automation rules and assist developers in generating codes for conflict detection, thereby enhancing their experience. A code adapter has been designed to separate logical reasoning from the syntactic details of code generation, enabling LLMs to generate code for programming languages beyond their training data. Finally, we evaluated the performance of AutoIoT and presented a case study demonstrating how AutoIoT can integrate with existing IoT platforms.

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