Role of Deep Learning in Mobile Ad-hoc Networks

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Sequence to Sequence Architecture-based Encoder-decoder
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Published: Dec 31, 2022
DOI: https://doi.org/10.17762/ijritcc.v10i2s.5938
Keywords:
LSTM, GRUs, DeepChannel, Deep Neural Networks, Deep Q-learning Algorithm, MANETs, Clustering Algorithm

Main Article Content

Vijay U. Rathod
Research Scholar, Department of Computer Engineering, MET’s Institute of Engineering, Nashik, SPPU, Pune Maharashtra, India
Shyamrao V. Gumaste
Professor, Department of Computer Engineering, MET’s Institute of Engineering, Nashik, SPPU, Pune Maharashtra, India

Abstract

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security.

Article Details

How to Cite
Rathod, V. U. ., & Gumaste, S. V. . (2022). Role of Deep Learning in Mobile Ad-hoc Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 10(2s), 237–246. https://doi.org/10.17762/ijritcc.v10i2s.5938
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Vol. 10 No. 2s (2022): Computer Science Applications: Innovation and Trends
Section
Articles

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