Comparative Analysis and Enhancement of Machine Learning Algorithms for Network Traffic Prediction in VANET
TL;DR
MGRU outperforms MNB, as MGU achieves high accuracy, high absolute throughput and packet delivery ratio, and low delay, reflecting better temporal modelling, as well as enhancing GRU and Naïve Bayes into Modified Naïve Bayes to improve VANET.
Abstract
Purpose: The study assessed the impact that machine learning algorithms, such as Gated Recurrent Unit and Naive Bayes, have on the performance of VANET. Design/Methodology/Approach: Vehicular Ad hoc networks are formed by vehicles themselves, enabling communication between vehicles (V2V) and the roadside infrastructure (V2I), and allowing vehicles to accurately receive real-time information about the safety status of their surroundings and traffic flow. A dataset comprising network metrics, including timestamps for sent and received packets, average delay, and energy consumption, as well as performance metrics such as Packet Delivery Ratio, Throughput, and congestion state, was utilised to establish both input features and evaluation benchmarks in the study. Research Limitation: Actual hardware implementation is difficult, which is a limitation Findings: The baseline (GRU) was improved to (MGRU), i.e. modified GRU by +12.64%, and the baseline (NB) was improved to (MNB), i.e. modified Naïve Bayes by +18.33%. MGRU outperforms MNB, as MGU achieves high accuracy, high absolute throughput and packet delivery ratio, and low delay, reflecting better temporal modelling. Practical Implication: The NS2 software was used to implement the VANET scenario. Social Implication: This research will help in traffic jams and traffic congestion situations in vehicular communication. Originality/Value: GRU was enhanced by modifying GRU and Naïve Bayes (NB) into Modified Naïve Bayes (MNB) to improve VANET.