DIB - A Novel Optimized VANET Traffic Management Using a Deep Neural Network

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7657
Keywords:
Deep Neural Network, Intelligent Transport System, Internet of Things, Machine Learning, VANET

Main Article Content

P. Sankar
Research Scholar, Department of Computer Applications, PRIST University, Vallam, Thanjavur, India
M. Robinson Joel
Associate Professor, Department of Information Technology, Kings Engineering College, Chennai, India
A. Jahir Husain
Assistant Professor, Department of Computer Applications, B.S.Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, India

Abstract

The advancement of the Internet of Things (IoT) establishes the development of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS).  An integral part of ITS is the vehicular ad hoc network (VANET) with smart vehicles (SV).   In this research, a dynamic method of traffic regulation in VANET is presented using Deep Neural Networks (DNN) and Bat Algorithms (BA). With a reduced average delay, the former (DNN) is utilized to guide vehicles across very crowded routes to increase efficiency. In order to examine the traffic congestion status between network nodes, BA is integrated with the IoT and moved over VANETs. Experiments were conducted to test the effectiveness of the proposed method with various parameters such as average latency, packet delivery ratio (PDR) and throughput and the performance were compared with different machine learning (ML) algorithms.  The simulation outputs proved that the proposed technique supports real-time traffic circumstances with less energy usage and delay than existing methods.

Article Details

How to Cite
Sankar, P. ., Joel, M. R. ., & Husain, A. J. . (2023). DIB - A Novel Optimized VANET Traffic Management Using a Deep Neural Network. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 484–491. https://doi.org/10.17762/ijritcc.v11i10s.7657
Issue
Vol. 11 No. 10s (2023)
Section
Articles

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