Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey

• URL: http://arxiv.org/abs/2504.07635v1
• Date: Thu, 10 Apr 2025 10:32:18 GMT
• Title: Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey
• Authors: Fabrizio Mangione, Claudio Savaglio, Giancarlo Fortino,
• Abstract summary: The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest.<n>This survey aims to provide a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms.
• Score: 7.8189600848712635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from the continuous evolution and widespread adoption they are both having individually, enough to spontaneously reshape numerous sectors, including Healthcare, Manufacturing, and Smart Cities. Hence, their increasing popularity has catalyzed further extensive research for understanding the potential of the duo GenAI-IoT, how they interplay, and to which extent their synergy can innovate the state-of-the-art in their individual scenarios. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. This survey exactly aims to address this gap by providing a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms. Our contribution is realized through a systematic literature review following the PRISMA methodology. A comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the past, present, and future directions of GenAI integration with IoT Computing, offering valuable insights for both experts and newcomers.

Related papers

• Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook [85.43403500874889]
  Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI)<n>Recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains.
  arXiv  Detail & Related papers  (2025-03-23T10:33:28Z)
• Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research [1.6311895940869516]
  We consider the evolving and emerging trends of AI in order to examine its present and predict its future impacts.<n>We explore the unique features of GenAI, which are rooted in the continued change from symbolism to connectionism.
  arXiv  Detail & Related papers  (2025-02-25T16:34:23Z)
• Generative AI for Internet of Things Security: Challenges and Opportunities [6.291311608742319]
  This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT.<n>Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further.<n>It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations.
  arXiv  Detail & Related papers  (2025-02-13T01:55:43Z)
• Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey [49.29751866761522]
  This paper aims to investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field. Then, an overview of the latest GenAI models is systematically reviewed. Finally, the corresponding applications in SAR domain are also included.
  arXiv  Detail & Related papers  (2024-11-05T03:06:00Z)
• Artificial Intelligence of Things: A Survey [14.204632921719933]
  The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT) We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains.
  arXiv  Detail & Related papers  (2024-10-25T22:45:58Z)
• Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI [0.0]
  This paper explores the nuanced landscape of generative AI (genAI) It focuses on neural network-based models like Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-10-22T02:18:44Z)
• Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision [76.4345564864002]
  Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. We propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents. We present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis.
  arXiv  Detail & Related papers  (2024-04-13T02:39:36Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

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.