IoT Application Development and Generation: From Low-Code to AI-Native

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.