IoT application development and generation: from low-code to AI-native

Shu Fanglei; Liu Jiawei; Li Borui; Wang Shuai

doi:10.11959/j.issn.2096-3750.2026.00510

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IoT application development and generation: from low-code to AI-native

Theory and Technology | 更新时间：2026-04-16

- IoT application development and generation: from low-code to AI-native
- Chinese Journal on Internet of Things Vol. 10, Issue 1, Pages: 112-124(2026)
- 作者机构：
  
  东南大学计算机科学与工程学院，江苏南京 211189
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62302096;62272098;U24B20152)
- DOI：10.11959/j.issn.2096-3750.2026.00510
  CLC： TP393.5;TP311.5
- Received：26 February 2025，
  
  Revised：2025-06-17，
  
  Accepted：18 July 2025，
  
  Published：30 March 2026
- 稿件说明：
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束方磊,刘佳伟,李博睿等.物联网应用开发与生成：从低代码到智能原生[J].物联网学报,2026,10(01):112-124.

Shu Fanglei,Liu Jiawei,Li Borui,et al.IoT application development and generation: from low-code to AI-native[J].Chinese Journal on Internet of Things,2026,10(01):112-124.
束方磊,刘佳伟,李博睿等.物联网应用开发与生成：从低代码到智能原生[J].物联网学报,2026,10(01):112-124. DOI： 10.11959/j.issn.2096-3750.2026.00510.

Shu Fanglei,Liu Jiawei,Li Borui,et al.IoT application development and generation: from low-code to AI-native[J].Chinese Journal on Internet of Things,2026,10(01):112-124. DOI： 10.11959/j.issn.2096-3750.2026.00510.

摘要

物联网（IoT

Internet of things）技术通过设备、传感器和网络的互联，实现了物与物、物与人之间的智能交互和数据共享，已广泛应用于智能家居、智慧交通和工业自动化等领域。传统的物联网开发流程依赖于人工的软硬协同开发，具有较高的技术门槛。低代码开发技术通过图形化编程界面和高度抽象的编程接口，显著地降低了开发门槛，但仍存在定制化能力不足的问题。随着以大语言模型为代表的人工智能（AI

artificial intelligence）及其相关技术的成熟，一种利用人工智能模型对物联网应用开发流程进行高层语义表征的“智能原生”物联网计算任务生成范式逐渐兴起，成为物联网应用开发的新机遇。为此，在梳理物联网应用开发技术的发展的基础上，提出了智能原生物联网计算任务生成框架，将开发过程划分为物联网应用计算意图解析与计算规划生成两个阶段。在此基础上，系统性地分析了智能原生物联网应用生成的关键技术和挑战，探讨了最新研究进展，最后对未来发展方向进行了展望。

Abstract

Internet of things (IoT) enables intelligent interaction and data sharing

facilitating connectivity among objects and humans. It has been widely applied in smart homes

intelligent transportation

industrial automation

etc. Traditional IoT development processes rely on manual hardware-software collaborative development

which involves a high technical barrier. Low-code development technology significantly lowers this barrier by providing graphical interfaces and highly abstracted programming application programming interfaces. Nevertheless

it still faces limitations in customization capabilities. With the maturity of artificial intelligence (AI) and related technologies represented by large language models

an emerging AI-native paradigm for IoT computing task generation characterized by leveraging AI models to provide high-level semantic representations of IoT application development workflows is gaining traction and presenting novel opportunities for IoT application development. Therefore

on the basis of reviewing the development of software development technology for the IoT

an AI-native framework for generating computing tasks of IoT was proposed in this paper

which was divided into two stages: intent understanding and task planning. Based on this

the key technologies and challenges were systematically analyzed

a comprehensive review of the latest research was provided

and the future development directions of AI-native computing task generation were finally looked forward to.

关键词

Keywords

references

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