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

• URL: http://arxiv.org/abs/2210.12944v1
• Date: Mon, 24 Oct 2022 04:18:57 GMT
• Title: Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges
• Authors: Prabath Abeysekara, Hai Dong, A.K. Qin
• Abstract summary: We present a novel computing vision named Deep Edge Intelligence (DEI) It employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. It aims to provision reliable and secure intelligence services to every person and organisation at any place with better user experience.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Artificial Intelligence for the Metaverse: A Survey [66.57225253532748]
  We first deliver a preliminary of AI, including machine learning algorithms and deep learning architectures, and its role in the metaverse. We then convey a comprehensive investigation of AI-based methods concerning six technical aspects that have potentials for the metaverse. Several AI-aided applications, such as healthcare, manufacturing, smart cities, and gaming, are studied to be deployed in the virtual worlds.
  arXiv  Detail & Related papers  (2022-02-15T03:34:56Z)
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [39.223546118441476]
  6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence" Deep learning and big data analytics based AI systems require tremendous computation and communication resources. edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
  arXiv  Detail & Related papers  (2021-11-24T11:47:16Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)
• Deep Learning in the Era of Edge Computing: Challenges and Opportunities [21.638476468152312]
  We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. Deep learning-based approaches require a large volume of high-quality data to train and are very expensive in terms of computation, memory, and power consumption.
  arXiv  Detail & Related papers  (2020-10-17T20:50:42Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface [35.64178319119883]
  We develop an over-the-air based communication-efficient federated machine learning framework for intelligent IoT networks. We exploit the waveform superposition property of a multi-access channel to reduce the model aggregation error.
  arXiv  Detail & Related papers  (2020-04-13T09:48:04Z)
• Communication-Efficient Edge AI: Algorithms and Systems [39.28788394839187]
  Wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data. Such enormous data cannot all be sent from end devices to the cloud for processing. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
  arXiv  Detail & Related papers  (2020-02-22T09:27:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.