AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles

TL;DR

This paper proposes a novel AI-driven traffic control scheme for IoV that dynamically allocates resources based on traffic class and delay requirements, improving access success rates.

Contribution

It introduces a hybrid traffic control scheme using PPO to optimize access class barring factors considering traffic dynamics and heterogeneous IoV application needs.

Findings

Improved successful transmission rate compared to existing schemes

Reduced access delay in simulated environments

Effective handling of diverse traffic classes

Abstract

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.