Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

By Shuiguang Deng, Hailiang Zhao, Jianwei Yin, Schahram Dustdar, Albert Y. Zomaya. IEEE Internet of Things Journal, 2019

799 citations recorded by the source corpus.

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, edge computing, is surging in popularity. Meanwhile, the artificial intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate edge computing and AI, which gives birth to edge intelligence. In this article, we divide edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). The former focuses on providing more optimal solutions to key problems in edge computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This article provides insights into this new interdisciplinary field from a broader perspective. It discusses the core concepts and the research roadmap, which should provide the necessary background for potential future research initiatives in edge intelligence.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/40fb2468c3a77c68fe703a6e614f4ad25bd4e3dd

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Citations in the corpus

6G Internet of Things: A Comprehensive Survey (2021, background): “This leads to a new paradigm called edge intelligence [38], [39], which is envisioned by the convergence of AI, communications, and edge computation.”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, background): “AI is playing an important role in the optimization of EC [52].”
AI for Next Generation Computing: Emerging Trends and Future Directions (2022, positive): “Micro data centres, rather than edge workloads, are preferred by most service providers in emerging economies, as they are more cost-effective [112].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “, to carry out AI models on the network edge) [146], [147].”
SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead (2019, background): “, Edge Intelligence [11], [12]) has emerged as a new focus of research ever since we began to realize the potential benefits of sinking the computation to and outside the edge whilst the increasing capacity of end devices makes it natural to empower them with AI and support the applications such as ”
Communication-Efficient Edge AI: Algorithms and Systems (2020, background): “Unlike existing survey papers [9], [10], [39], [40], we shall present a comprehensive coverage to address the communication challenges for realizing AI at network edges.”
The Roadmap to 6G Security and Privacy (2021, background): “The union between AI and edge computing is instinctive since there is a close interaction [31].”