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ChatIoT: Zero-code Generation of Trigger-action Based IoT Programs with ChatGPT

Authors:

Fu Li,

Jiaming Huang,

Yi Gao,

Wei DongAuthors Info & Claims

APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking

Pages 219 - 220

https://doi.org/10.1145/3600061.3603141

Published: 05 September 2023 Publication History

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Abstract

Trigger-Action Program (TAP) is a popular and significant form of Internet of Things (IoT) applications, commonly utilized in smart homes. Existing works either just perform actions based on commands or require human intervention to generate TAPs. With the emergence of Large Language Models (LLMs), it becomes possible for users to create IoT TAPs in zero-code manner using natural language. Thus, we propose ChatIoT, which employs LLMs to process natural language in chats and realizes the zero-code generation of TAPs for existing devices.

References

[1]

Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International Conference on Machine Learning. PMLR, 12888–12900.

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[2]

Chris Quirk, Raymond Mooney, and Michel Galley. 2015. Language to code: Learning semantic parsers for if-this-then-that recipes. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 878–888.

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[3]

Imam Nur Bani Yusuf, Lingxiao Jiang, and David Lo. 2022. Accurate generation of trigger-action programs with domain-adapted sequence-to-sequence learning. In Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension. 99–110.

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Cited By

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Giudici MPadalino LPaolino GParatici IPascu AGarzotto F(2024)Designing Home Automation Routines Using an LLM-Based ChatbotDesigns10.3390/designs80300438:3(43)Online publication date: 13-May-2024
https://doi.org/10.3390/designs8030043
Balasubramanian PSeby JKostakos P(2024)CYGENT: A cybersecurity conversational agent with log summarization powered by GPT-32024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574658(1-6)Online publication date: 3-May-2024
https://doi.org/10.1109/AIIoT58432.2024.10574658

Index Terms

ChatIoT: Zero-code Generation of Trigger-action Based IoT Programs with ChatGPT
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Helping Users Debug Trigger-Action Programs

Trigger-action programming (TAP) empowers a wide array of users to automate Internet of Things (IoT) devices. However, it can be challenging for users to create completely correct trigger-action programs (TAPs) on the first try, necessitating debugging. ...
ChatIoT: Zero-code Generation of Trigger-action Based IoT Programs

Trigger-Action Program (TAP) is a simple but powerful format to realize intelligent IoT applications, especially in home automation scenarios. Existing trace-driven approaches and in-situ programming approaches depend on either customized interaction ...
End-User Programming in Smart Homes with Trigger-Action Programs

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Published In

APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking

June 2023

229 pages

ISBN:9798400707827

DOI:10.1145/3600061

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2023

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Poster
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APNET 2023

APNET 2023: 7th Asia-Pacific Workshop on Networking

June 29 - 30, 2023

Hong Kong, China

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Overall Acceptance Rate 50 of 118 submissions, 42%

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Cited By

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Giudici MPadalino LPaolino GParatici IPascu AGarzotto F(2024)Designing Home Automation Routines Using an LLM-Based ChatbotDesigns10.3390/designs80300438:3(43)Online publication date: 13-May-2024
https://doi.org/10.3390/designs8030043
Balasubramanian PSeby JKostakos P(2024)CYGENT: A cybersecurity conversational agent with log summarization powered by GPT-32024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574658(1-6)Online publication date: 3-May-2024
https://doi.org/10.1109/AIIoT58432.2024.10574658

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Helping Users Debug Trigger-Action Programs