Special issue on integrative horizons: charting new paths in the convergence of foundation models and IoT

Published 12 April, 2024


The convergence of the Internet-of-Things (IoT) and foundation models, particularly Large Language Models (LLMs), is believed to mark a new era of intelligent and adaptive systems. This fusion highlights the potential to revolutionize how we interact with technology, making it more intuitive and capable of autonomously managing complex tasks with minimal human intervention. The IoT’s extensive network of interconnected devices offers a wealth of real-time data, which, when paired with the advanced processing power of foundation models like LLMs, unlocks new avenues for innovation across various sectors.

This special issue is designed to explore this emerging field, focusing on the architectural, operational, and application-related intricacies of this integration. It seeks contributions that showcase the enhanced decision-making and automation capabilities afforded by this synergy and addresses the significant challenges it faces, including data privacy, scalability, and the need for scalable, secure, robust architectures. By highlighting innovative solutions and real-world applications, this issue strives to build a holistic view of the opportunities and obstacles in the IoT and foundational model nexus, setting the stage for a future where technology is seamlessly woven into the fabric of our daily existence. Furthermore, this special issue aims to catalyze industry-academic discourse, propel research, and foster innovation at the confluence of IoT and foundational models.  In this context, we invite original contributions on a wide array of topics。

Topics covered include, but are not limited to:

  • Novel theories, concepts, and paradigms of the convergence of IoT and foundation models Architectural blueprints for weaving IoT with foundational models
  • Architectural blueprints for weaving IoT with foundational models.
  • Scalable edge computing models enhancing IoT and LLM synergies.
  • Scalable Edge Computing Solutions for IoT-LLM Integration
  • Novel Hardware Acceleration Techniques for IoT Devices Utilizing LLMs
  • Big Data Analytics Powered by LLMs for IoT Insights
  • Adaptive Resource Management in IoT Using Foundation Models
  • Deployment and Orchestration of IoT Services with foundation model Support
  • Containerization tactics for streamlined IoT-LLM application deployment
  • Serverless Computing Models for Dynamic IoT-LLM Workloads
  • Distributed Execution Environments for IoT and LLM Collaboration
  • Data collection, aggregation, and analysis techniques enriched by foundational models
  • Orchestrating AI and IoT workflows with foundation models for complex task execution
  • Development of IoT-focused foundational and generative AI models.
  • Innovations in achieving interpretability and explainability in IoT
  • Context-aware data enrichment techniques using foundational models
  • Investigation into the fusion of IoT data streams with foundation models (LLMs) for enhanced decision-making
  • Privacy-preserving machine learning techniques
  • Data ownership management
  • Security, Privacy, and Trustworthiness
  • Modern learning algorithms tailored for security, privacy preservation, and trustworthiness
  • Adversarial examples of attacks and defense
  • The role of 5G/6G, Edge-Cloud computing, and Blockchain in the convergence of IoT and foundation models
  • Foundation model and generative AI applications in IoT for dynamic analytics and creative problem-solving
  • Cross-disciplinary methods for evaluating and refining IoT-integrated foundation models
  • Grounded learning - Comparative studies on learning modalities in the context of IoT and foundational model integration
  • Social and interactive learning paradigms within IoT and foundational model ecosystems
  • Sector-specific applications and case studies (e.g., Healthcare, Industry 4.0, Energy, Smart Cities, Finance)

Important dates:

  • Submissions deadline: September 1, 2024
  • First notification: November 1, 2024
  • Final notification:March 31, 2025
  • Publication date: April2025

Submission instructions:

Please read the Guide for Authors before submitting. All articles should be submitted online via the editorial management system; please select article type: Foundation Models and IoT.

Guest editors:

• Farshad Firouzi, Duke University, USA. Email: Farshad.firouzi@duke.edu)

• Mahmoud Daneshmand, Stevens Institute of Technology, USA. Email: mdaneshm@stevens.edu

• JaeSeung Song, Sejong University, South Korea. Email: jssong@sejong.ac.kr

• Shaoen Wu, Kennesaw State University, USA. Email: swu10@kennesaw.edu


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