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