eprintid: 19632
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/96/32
datestamp: 2024-06-04 14:19:22
lastmod: 2024-06-04 14:19:22
status_changed: 2024-06-04 14:15:28
type: article
metadata_visibility: show
creators_name: Mustafa, E.
creators_name: Shuja, J.
creators_name: Rehman, F.
creators_name: Riaz, A.
creators_name: Maray, M.
creators_name: Bilal, M.
creators_name: Khan, M.K.
title: Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues
ispublished: pub
keywords: Antennas; Computation offloading; Delay-sensitive applications; Mobile edge computing; Numerical methods; Optimization; Reinforcement learning; Taxonomies, Computation offloading; Computing resource; Delay-sensitive applications; EDGE Networks; Edge server; Modern paradigms; Network applications; Network edges; Reinforcement learnings; Storage resources, Deep neural networks
note: cited By 0
abstract: Mobile Edge Computing (MEC) is a modern paradigm that involves moving computing and storage resources closer to the network edge, reducing latency, and enabling innovative, delay-sensitive applications. Within MEC, computation offloading refers to the process of transferring computationally intensive tasks or processes from mobile devices to edge servers, optimizing the performance of mobile applications. Traditional numerical optimization methods for computation offloading often necessitate numerous iterations to attain optimal solutions. In this paper, we provide a tutorial on how Deep Neural Networks (DNNs) resolve the challenges of computation offloading. The article explores various applications of DNNs in computation offloading, encompassing channel estimation, caching, AR and VR applications, resource allocation, mode selection, unmanned aerial vehicles (UAVs), and vehicle management. We present a comprehensive taxonomy that categorizes these applications, and offer an overview of existing schemes, comparing their effectiveness. Additionally, we outline the open research issues that can be addressed through the application of DNNs in MEC offloading. We also highlight specific challenges related to DNN utilization in computation offloading. In conclusion, we affirm that DNNs are widely acknowledged as invaluable tools for optimizing computation offloading in MEC. © 2024 Elsevier Ltd
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191587173&doi=10.1016%2fj.jnca.2024.103886&partnerID=40&md5=ef65fd26cc57d5521f5d839c875e2111
id_number: 10.1016/j.jnca.2024.103886
full_text_status: none
publication: Journal of Network and Computer Applications
volume: 226
refereed: TRUE
citation:   Mustafa, E. and Shuja, J. and Rehman, F. and Riaz, A. and Maray, M. and Bilal, M. and Khan, M.K.  (2024) Deep Neural Networks meet computation offloading in mobile edge networks: Applications, taxonomy, and open issues.  Journal of Network and Computer Applications, 226.