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Communication technologies for edge learning and inference: a novel framework, open issues, and perspectives

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posted on 2023-06-10, 05:22 authored by Khan Muhammad, Javier Del Ser, Naercio Magaia, Ramon Fonseca, Tanveer Hussain, Amir Gandomi, Mahmoud Daneshmand, Victor de Albuquerque
With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving towards the edge of the network. Due to numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the Edge Computing paradigm. Together with Machine Learning, Edge Computing has turned into a powerful local decision-making tool, thus fostering the advent of Edge Learning. The latter, however, has become delay-sensitive as well as resource-thirsty in terms of hardware and networking. New methods have been developed to solve or, at least, minimize these issues, as proposed in this research. In this study, we first investigate representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we propose an ELI-based video data prioritization framework which only considers the data having events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, in this overview, we critically examine various communication aspects related to Edge Learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss challenges and present issues that are yet to be overcome.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Network: the magazine of global information exchange

ISSN

1558-156X

Publisher

IEEE

Page range

1-7

Department affiliated with

  • Informatics Publications

Research groups affiliated with

  • Foundations of Software Systems Publications

Notes

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2022-11-10

First Open Access (FOA) Date

2022-11-10

First Compliant Deposit (FCD) Date

2022-11-08

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