Traffic management in vehicular adhoc networks using hybrid deep neural networks and mobile agents

Abstract

The traffic congestion in vehicular adhoc networks (VANETs) is a vital problem due to its dynamic increase in traffic loads. VANETs undergo inefficient routing capability due to its increasing traffic demands. This has led to the need for intelligent transport system (ITS) to assist VANETs in enabling suitable traffic loads between vehicles and road side units (RSU). Most conventional systems offer distributed solution to manage traffic congestion but fail to regulate real-time traffic flows. In this paper, a dynamic traffic control in VANETs is offered by combining deep neural network (DNN) with mobile agents (MA). An experimental analysis is carried out to test the efficacy of the DNN-MA against conventional machine learning and a deep learning routing algorithm in VANETs. DNN-Mal is validated under various traffic congestion metrics like latency, percentage delivery ratio, packet error rate, and throughput. The results show that the proposed method offers reduced energy consumption and latency.