An Innovative Resource Management Framework using Logit-Boosted Machine Learning Algorithms for Vehicular Ad Hoc Networks (VANETs) 

Wenye Zhang1

Sergey Evgenievich Barykin1,Email

Tatiana Viktorovna Kirillova1

Irina Vasilievna Kapustina1

Nikita Sergeevich Lukashevich2

Andrey Zaytsev3

1Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia.
2Graduate School of Industrial Management, Peter the Great St.Petersburg Polytechnic University, St. Petersburg, Russia.
3Graduate School of Industrial Economics, Peter the Great Saint Petersburg Polytechnic University, Saint Petersburg, Russian Federation.

 

Abstract

Vehicular ad hoc networks (VANETs) are crucial to the evolution of transportation because they improve traffic flow, boost safety, and enable cutting-edge uses of vehicles. This research makes use of the power of logit-boosted machine learning techniques to create a full-fledged resource management system specifically designed for edge computing in VANETs. Our architectural masterwork consists of a resource predictor, allocator, and performance evaluator, and it excels in accurately predicting the availability of resources, allocating them strategically to where they are most required, and rigorously assessing the resulting network's efficacy. This innovative study demonstrates extraordinary decreases in network latency, considerable increases in resource usage, equitable resource allocation, huge throughput boosts, and major improvements in application performance. Not only does our approach outperform previous studies in terms of efficiency and scalability, but it also paves the way for game-changing applications in the automobile sector, which promises to produce safer streets and a more effective transportation network. This study is the first to investigate the use of logit-boosted machine learning algorithms in the context of VANET resource management; as such, it paves the way for future intelligent traffic systems.

An Innovative Resource Management Framework using Logit-Boosted Machine Learning Algorithms for Vehicular Ad Hoc Networks (VANETs)