Analytical Study of Task Offloading Techniques using Deep Learning

SMART MOVES JOURNAL IJOSCIENCE

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Field Value
 
Title Analytical Study of Task Offloading Techniques using Deep Learning
 
Creator Almelu, Mr
Veenadhari, Dr. S.
Maheshwar, Kamini
 
Subject Internet of Things (IoT), Bandwidth, Task Offloading, Deep Learning.
 
Description The Internet of Things (IoT) systems create a large amount of sensing information. The consistency of this information is an essential problem for ensuring the quality of IoT services. The IoT data, however, generally suffers due to a variety of factors such as collisions, unstable network communication, noise, manual system closure, incomplete values and equipment failure. Due to excessive latency, bandwidth limitations, and high communication costs, transferring all IoT data to the cloud to solve the missing data problem may have a detrimental impact on network performance and service quality. As a result, the issue of missing information should be addressed as soon as feasible by offloading duties like data prediction or estimations closer to the source. As a result, the issue of incomplete information must be addressed as soon as feasible by offloading duties such as predictions or assessment to the network’s edge devices. In this work, we show how deep learning may be used to offload tasks in IoT applications.
 
Publisher SMART MOVES
 
Date 2021-07-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijoscience.com/ojsscience/index.php/ojsscience/article/view/393
10.24113/ijoscience.v7i7.393
 
Source SMART MOVES JOURNAL IJOSCIENCE; Volume 7, Issue 7, July 2021; 1-4
2582-4600
 
Language eng
 
Relation http://ijoscience.com/ojsscience/index.php/ojsscience/article/view/393/915
 
Rights Copyright (c) 2021 Almelu, Dr. S. Veenadhari, Kamini Maheshwar
http://creativecommons.org/licenses/by/4.0
 

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