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

TL;DR

This survey reviews the architectures, technologies, and collaboration paradigms enabling cloud-edge-terminal intelligence in AIoT networks, highlighting recent advances, challenges, and future directions for distributed AI applications.

Contribution

It provides a comprehensive tutorial-style overview of CETCI architectures, core technologies, and intelligent collaboration frameworks in AIoT, integrating recent research and future trends.

Findings

Analyzes architectural components across cloud, edge, and terminal layers.

Reviews advances in federated learning, distributed deep learning, and reinforcement learning.

Discusses challenges like scalability, heterogeneity, and interoperability.

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.