A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks

2025-08-26

AI Summary

In AIoT networks, inefficient task offloading, imbalanced resource allocation, and limited scalability of collaborative intelligence persist under heterogeneous cloud–edge–end environments. Method: This paper proposes a Cloud–Edge–Thing Collaborative Intelligence (CETCI) architecture and a systematic methodology. It introduces a taxonomy of collaborative intelligence tailored for AIoT, integrating federated learning, distributed deep learning, and reinforcement learning frameworks. Leveraging network virtualization, container orchestration, software-defined networking (SDN), and evolutionary edge-cloud modeling, the approach enables end-to-end collaborative optimization. Contributions: (1) The first comprehensive CETCI technology map covering full-stack technical elements; (2) A collaborative design guideline balancing security, interoperability, and scalability; (3) A forward-looking roadmap for CETCI evolution driven by 6G+, intelligent agents, and digital twins—providing both theoretical foundations and practical paradigms for deployable AIoT systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven services, has increased demands for efficient distributed computing architectures and networks, driving cloud-edge-terminal collaborative intelligence (CETCI) as a fundamental paradigm within the artificial intelligence of things (AIoT) community. With advancements in deep learning, large language models (LLMs), and edge computing, CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems for AIoT (CISAIOT), a practical research focus in AI, distributed computing, and communications. This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners. We systematically analyze architectural components spanning cloud, edge, and terminal layers, examining core technologies including network virtualization, container orchestration, and software-defined networking, while presenting categorizations of collaboration paradigms that cover task offloading, resource allocation, and optimization across heterogeneous infrastructures. Furthermore, we explain intelligent collaboration learning frameworks by reviewing advances in federated learning, distributed deep learning, edge-cloud model evolution, and reinforcement learning-based methods. Finally, we discuss challenges (e.g., scalability, heterogeneity, interoperability) and future trends (e.g., 6G+, agents, quantum computing, digital twin), highlighting how integration of distributed computing and communication can address open issues and guide development of robust, efficient, and secure collaborative AIoT systems.

Problem

• Surveying cloud-edge-terminal collaborative intelligence architectures
• Analyzing enabling technologies for AIoT distributed systems
• Addressing challenges in scalable heterogeneous AIoT integration

Innovation

• Cloud-edge-terminal collaborative intelligence architecture
• Network virtualization and container orchestration technologies
• Federated learning and distributed deep learning frameworks