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  "dcterms:identifier": [
    "tag:aim-pro.eu,2026:oer/3eef6c3c532c",
    "https://arxiv.org/abs/2506.22477",
    "arxiv:2506.22477",
    "doi:10.1109/RICAI64321.2024.10911316"
  ],
  "dcterms:title": [
    "Innovative Research on IoT Architecture and Robotic Operating Platforms: Applications of Large Language Models and Generative AI"
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  "dcterms:type": [
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    "preprint"
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  "dcterms:creator": [
    "Huiwen Han"
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  "dcterms:description": [
    "This paper introduces an innovative design for robotic operating platforms, underpinned by a transformative Internet of Things (IoT) architecture, seamlessly integrating cutting-edge technologies such as large language models (LLMs), generative AI, edge computing, and 5G networks. The proposed platform aims to elevate the intelligence and autonomy of IoT systems and robotics, enabling them to make real-time decisions and adapt dynamically to changing environments. Through a series of compelling case studies across industries including smart manufacturing, healthcare, and service sectors, this paper demonstrates the substantial potential of IoT-enabled robotics to optimize operational workflows, enhance productivity, and deliver innovative, scalable solutions. By emphasizing the roles of LLMs and generative AI, the research highlights how these technologies drive the evolution of intelligent robotics and IoT, shaping the future of industry-specific advancements. The findings not only showcase the transformative power of these technologies but also offer a forward-looking perspective on their broader societal and industrial implications, positioning them as catalysts for next-generation automation and technological convergence."
  ],
  "dcterms:subject": [
    "cs.NI (arxiv)",
    "cs.AI (arxiv)",
    "cs.ET (arxiv)",
    "cs.RO (arxiv)"
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  "dcterms:language": [
    "en"
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  "dcterms:license": [
    {
      "@id": "http://creativecommons.org/licenses/by/4.0/"
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  "dcterms:rights": [
    "CC-BY-4.0 — assessed as OPEN by the harvester's licence gate. Conditions: attribution required. open, permissive"
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    "open"
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    "text/markdown"
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  "dcterms:extent": [
    "415911 bytes (original)",
    "26471 bytes (extracted text)"
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  "dcterms:issued": [
    "2025-06-22"
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  "dcterms:bibliographicCitation": [
    "2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI), 2024, IEEE Xplore, pp. 881-886"
  ],
  "dcterms:provenance": [
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