Open AccessAnalytical Study of Task Offloading Techniques using Deep Learning
Author(s) -
Almelu,
S. Veenadhari,
Kamini Maheshwar
Publication year - 2021
Publication title -
smart moves journal ijoscience
Language(s) - English
Resource type - Journals
ISSN - 2582-4600
DOI - 10.24113/ijoscience.v7i7.393
Subject(s) - computer science , cloud computing , variety (cybernetics) , consistency (knowledge bases) , task (project management) , enhanced data rates for gsm evolution , computer network , edge computing , distributed computing , missing data , latency (audio) , bandwidth (computing) , internet of things , computer security , machine learning , artificial intelligence , telecommunications , engineering , systems engineering , operating system
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.