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Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges

arXiv 2210.12944 ↗First posted 24 Oct 2022cs.DC cs.AIcited by 0

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Abstract (arXiv)

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. The opportunity to combine these two domains of technologies to power interconnected devices with intelligence is likely to pave the way for a new wave of technology revolutions. Embracing this technology revolution, in this article, we present a novel computing vision named Deep Edge Intelligence (DEI). DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. aiming to provision reliable and secure intelligence services to every person and organisation at any place with better user experience. The vision, system architecture, key layers and features of DEI are also detailed. Finally, we reveal the key enabling technologies and research challenges associated with it.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 24 Oct 2022.

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References · 13

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1 more
  1. 2020Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environmentscited by 1

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