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Revisiting edge AI : opportunities and challenges

Tobias Meuser, Lauri Loven, Monowar Bhuyan, Shishir G. Patil, Schahram Dustdar, Atakan Aral, Suzan Bayhan, Christian Becker, Eyal de Lara, Aaron Yi Ding, et al.
(2024) IEEE INTERNET COMPUTING. 28(4). p.49-59
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Abstract
Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.
Keywords
Artificial intelligence, Edge computing, Training, Machine learning, Inference algorithms, Data collection, Data models

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MLA
Meuser, Tobias, et al. “Revisiting Edge AI : Opportunities and Challenges.” IEEE INTERNET COMPUTING, vol. 28, no. 4, 2024, pp. 49–59, doi:10.1109/MIC.2024.3383758.
APA
Meuser, T., Loven, L., Bhuyan, M., Patil, S. G., Dustdar, S., Aral, A., … Welzl, M. (2024). Revisiting edge AI : opportunities and challenges. IEEE INTERNET COMPUTING, 28(4), 49–59. https://doi.org/10.1109/MIC.2024.3383758
Chicago author-date
Meuser, Tobias, Lauri Loven, Monowar Bhuyan, Shishir G. Patil, Schahram Dustdar, Atakan Aral, Suzan Bayhan, et al. 2024. “Revisiting Edge AI : Opportunities and Challenges.” IEEE INTERNET COMPUTING 28 (4): 49–59. https://doi.org/10.1109/MIC.2024.3383758.
Chicago author-date (all authors)
Meuser, Tobias, Lauri Loven, Monowar Bhuyan, Shishir G. Patil, Schahram Dustdar, Atakan Aral, Suzan Bayhan, Christian Becker, Eyal de Lara, Aaron Yi Ding, Janick Edinger, James Gross, Nitinder Mohan, Andy D. Pimentel, Etienne Riviere, Henning Schulzrinne, Pieter Simoens, Guerkan Solmaz, and Michael Welzl. 2024. “Revisiting Edge AI : Opportunities and Challenges.” IEEE INTERNET COMPUTING 28 (4): 49–59. doi:10.1109/MIC.2024.3383758.
Vancouver
1.
Meuser T, Loven L, Bhuyan M, Patil SG, Dustdar S, Aral A, et al. Revisiting edge AI : opportunities and challenges. IEEE INTERNET COMPUTING. 2024;28(4):49–59.
IEEE
[1]
T. Meuser et al., “Revisiting edge AI : opportunities and challenges,” IEEE INTERNET COMPUTING, vol. 28, no. 4, pp. 49–59, 2024.
@article{01J6VNKNPHM3PFM230CEWFH9V1,
  abstract     = {{Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.}},
  author       = {{Meuser, Tobias and  Loven, Lauri and  Bhuyan, Monowar and  Patil, Shishir G. and  Dustdar, Schahram and  Aral, Atakan and  Bayhan, Suzan and  Becker, Christian and  de Lara, Eyal and  Ding, Aaron Yi and  Edinger, Janick and  Gross, James and  Mohan, Nitinder and  Pimentel, Andy D. and  Riviere, Etienne and  Schulzrinne, Henning and Simoens, Pieter and  Solmaz, Guerkan and  Welzl, Michael}},
  issn         = {{1089-7801}},
  journal      = {{IEEE INTERNET COMPUTING}},
  keywords     = {{Artificial intelligence,Edge computing,Training,Machine learning,Inference algorithms,Data collection,Data models}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{49--59}},
  title        = {{Revisiting edge AI : opportunities and challenges}},
  url          = {{http://doi.org/10.1109/MIC.2024.3383758}},
  volume       = {{28}},
  year         = {{2024}},
}
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