RT Journal Article
T1 Communication Technologies for Edge Learning and Inference: 												A Novel Framework, Open Issues, and Perspectives
A1 Muhammad, Khan
A1 Ser, Javier Del
A1 Magaia, Naercio
A1 Fonseca, Ramon
A1 Hussain, Tanveer
A1 Gandomi, Amir H.
A1 Daneshmand, Mahmoud
A1 De Albuquerque, Victor Hugo C.
AB 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 toward the edge of the network. For 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 become a powerful local decision-making tool, fostering the advent of edge learning. However, the latter has become delay-sensitive and resource-Thirsty in terms of hardware and networking. New methods have been developed to solve or minimize these issues, as proposed in this study. We first investigated representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we proposed an ELI-based video data prioritization framework that only considers data with events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, we critically examined various communication aspects related to edge learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss the challenges and present issues that remain.
SN 0890-8044
YR 2023
FD 2023-03-01
LA eng
NO Muhammad , K , Ser , J D , Magaia , N , Fonseca , R , Hussain , T , Gandomi , A H , Daneshmand , M & De Albuquerque , V H C 2023 , ' Communication Technologies for Edge Learning and Inference : A Novel Framework, Open Issues, and Perspectives ' , IEEE Network , vol. 37 , no. 2 , pp. 246-252 . https://doi.org/10.1109/MNET.125.2100771
NO Publisher Copyright: © 1986-2012 IEEE.
NO This work was supported by the Basque Government via the ELKARTEK program (ref. KK-2022/00119), and consolidated research group MATHMODE (ref. IT1456-22).The work of Naercio Magaia was supported by H2020-MSCARISE under grant No. 101006411.
DS TECNALIA Publications
RD 28 sept 2024