AutoBridge: Automating Smart Device Integration with Centralized Platform

• URL: http://arxiv.org/abs/2507.23178v1
• Date: Thu, 31 Jul 2025 01:14:14 GMT
• Title: AutoBridge: Automating Smart Device Integration with Centralized Platform
• Authors: Siyuan Liu, Zhice Yang, Huangxun Chen,
• Abstract summary: AutoBridge implements a divide-and-conquer strategy to generate IoT integration code.<n>It can achieve an average success rate of 93.87% and an average function coverage of 94.87%, without any human involvement.<n>A user study with 15 participants shows that AutoBridge outperforms expert programmers by 50% to 80% in code accuracy.
• Score: 10.962240689805709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal IoT systems coordinate diverse IoT devices to deliver human-centered services. The ability to incorporate new IoT devices under the management of a centralized platform is an essential requirement. However, it requires significant human expertise and effort to program the complex IoT integration code that enables the platform to understand and control the device functions. Therefore, we propose AutoBridge to automate IoT integration code generation. Specifically, AutoBridge adopts a divide-and-conquer strategy: it first generates device control logic by progressively retrieving device-specific knowledge, then synthesizes platformcompliant integration code using platform-specific knowledge. To ensure correctness, AutoBridge features a multi-stage debugging pipeline, including an automated debugger for virtual IoT device testing and an interactive hardware-in-the-loop debugger that requires only binary user feedback (yes and no) for real-device verification. We evaluate AutoBridge on a benchmark of 34 IoT devices across two open-source IoT platforms. The results demonstrate that AutoBridge can achieves an average success rate of 93.87% and an average function coverage of 94.87%, without any human involvement. With minimal binary yes and no feedback from users, the code is then revised to reach 100% function coverage. A user study with 15 participants further shows that AutoBridge outperforms expert programmers by 50% to 80% in code accuracy, even when the programmers are allowed to use commercial code LLMs.

Related papers

• EmbedGenius: Towards Automated Software Development for Generic Embedded IoT Systems [11.524778651869044]
  This paper introduces EmbedGenius, the first fully automated software development platform for general-purpose embedded IoT systems.<n>The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process.<n>We evaluate EmbedGenius's performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks.
  arXiv  Detail & Related papers  (2024-12-12T08:34:12Z)
• AutoIoT: Automated IoT Platform Using Large Language Models [14.481067833984474]
  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.
  arXiv  Detail & Related papers  (2024-11-16T02:02:01Z)
• AutoGLM: Autonomous Foundation Agents for GUIs [51.276965515952]
  We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs) We have developed AutoGLM as a practical foundation agent system for real-world GUI interactions. Our evaluations demonstrate AutoGLM's effectiveness across multiple domains.
  arXiv  Detail & Related papers  (2024-10-28T17:05:10Z)
• IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
  IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio. Machine learning presents a rich opportunity to automatically process IoT data at scale. We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
  arXiv  Detail & Related papers  (2024-07-13T08:20:37Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey [82.84057882105931]
  Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) We present the fundamentals of GAI, MoE, and their interplay applications in IoV. We discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation.
  arXiv  Detail & Related papers  (2024-04-25T06:22:21Z)
• IoT Device Labeling Using Large Language Models [3.3044728148521623]
  We tackle a key challenge in IoT labeling: how can an AI solution label an IoT device that has never been seen before and whose label is unknown? Our solution extracts textual features such as domain names and catalogs from network traffic, and then enriches these features using Google search data alongside catalog of vendors and device functions. The solution also integrates an auto-update mechanism that uses Large Language Models (LLMs) to update these emerging device types.
  arXiv  Detail & Related papers  (2024-03-03T18:41:22Z)
• U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT [3.981958767941474]
  U-TOE is a universal toolkit designed to facilitate the task of IoT designers and researchers. We provide an open source implementation of U-TOE and demonstrate its use to experimentally evaluate the performance of various models.
  arXiv  Detail & Related papers  (2023-06-26T10:35:31Z)
• Composing Complex and Hybrid AI Solutions [52.00820391621739]
  We describe an extension of the Acumos system towards enabling the above features for general AI applications. Our extensions include support for more generic components with gRPC/Protobuf interfaces. We provide examples of deployable solutions and their interfaces.
  arXiv  Detail & Related papers  (2022-02-25T08:57:06Z)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• RL-IoT: Towards IoT Interoperability via Reinforcement Learning [3.939866872704532]
  We propose RL-IoT -- a system that explores how to interact with possibly unknown IoT devices. We leverage reinforcement learning to understand the semantics of protocol messages and to control the device to reach a given goal. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, completing non-trivial patterns with as few as 400 interactions.
  arXiv  Detail & Related papers  (2021-05-03T14:09:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.