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Published in: Computing 7/2023

22-01-2023 | Regular Paper

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Authors: Maryam Bagherian, Sarah Chehade, Ben Whitney, Ali Passian

Published in: Computing | Issue 7/2023

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Abstract

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. We present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.
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Metadata
Title
Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction
Authors
Maryam Bagherian
Sarah Chehade
Ben Whitney
Ali Passian
Publication date
22-01-2023
Publisher
Springer Vienna
Published in
Computing / Issue 7/2023
Print ISSN: 0010-485X
Electronic ISSN: 1436-5057
DOI
https://doi.org/10.1007/s00607-023-01154-0

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