Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 12, Issue 2


Published
on

April 5, 2021


Pages

1721-1728


DOI

Article

A Deep Learning method for effective channel allotment for SDN based IOT


Authors

Shilpa P Khedkar Affiliation:
Sathyabama Institute of Science and Technology, Department of Computer Science and Engineering, Chennai, 600119, India; M.E.S. College of Engineering, Pune, Department of Computer Engineering, SPPU University, Pune, 411001, India
and R Aroul Canessane Affiliation:
Sathyabama Institute of Science and Technology, Department of Computer Science and Engineering, Chennai, 600119, India


Abstract

Due to advances in the field of internet of things (IoT), the transmission speed become very important and need to be discussed. Doing proper assignment of appropriate channels to the generated traffic in SDN based IoT can affect transmission speed enormously. Software Defined Networking has been evolved as a supporting technology to improve the performance of IoT networks and to increase transmission quality. Different machine learning algorithm can be used for prediction of network traffic and allocation of the channel is done for better assignment. Hence, in this paper CNNs based network traffic prediction and allocation of channel technique is proposed. This technique significantly improves the network performance.


Keywords

IoT, SDN, Channel Assignment, machine learning, traffic load prediction, deep learning


Citation

Khedkar, S. P. & Canessane, R. A. (2021). A deep learning method for effective channel allotment for SDN based IOT. Turkish Journal of Computer and Mathematics Education, 12(2), 1721–1728.

Published by: Engineering Journals

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