Files and links (1)
SubmittedarXiv.org - Non-exclusive license to distribute, Open
Journal article
AutoIoT: Automated IoT Platform Using Large Language Models
IEEE internet of things journal, v 12(10), pp 1-1
2025
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.
Metrics
Details
- Title
- AutoIoT: Automated IoT Platform Using Large Language Models
- Creators
- Ye Cheng - Shandong University of Science and TechnologyMinghui Xu - Shandong University of Science and TechnologyYue Zhang - Drexel University, Computer Science (Computing)Kun Li - Shandong University of Science and TechnologyRuoxi Wang - Northeastern UniversityLian Yang - Shandong First Medical University
- Publication Details
- IEEE internet of things journal, v 12(10), pp 1-1
- Publisher
- IEEE
- Number of pages
- 1
- Grant note
- 2023HWYQ-008 / the Shandong Science Fund for Excellent Young Scholars ZR2022ZD02 / Natural Science Foundation of Shandong Province; Shandong Provincial Natural Science Foundation (10.13039/501100007129) 62302266; 62232010; U23A20302 / National Natural Science Foundation of China; the National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001484711600046
- Scopus ID
- 2-s2.0-85214297566
- Other Identifier
- 991022009891304721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Engineering, Electrical & Electronic
- Telecommunications