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Open Access

ARTICLE

DRG-DCC: A Driving Risk Gaming Based Distributed Congestion Control Method for C-V2X Technology

Lingqiu Zeng1, Peibing Sa1, Qingwen Han2, Lei Ye2,*, Letian Yang1, Cheng Zhang1, Jiqiang Cheng2
1 College of Computer Science, Chongqing University, Chongqing, 400044, China
2 School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400044, China
* Corresponding Author: Lei Ye. Email: email
(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.060392

Received 31 October 2024; Accepted 12 February 2025; Published online 21 March 2025

Abstract

Congestion control is an inherent challenge of V2X (Vehicle to Everything) technologies. Due to the use of a broadcasting mechanism, channel congestion becomes severe with the increase in vehicle density. The researchers suggested reducing the frequency of packet dissemination to relieve congestion, which caused a rise in road driving risk. Obviously, high-risk vehicles should be able to send messages timely to alarm surrounding vehicles. Therefore, packet dissemination frequency should be set according to the corresponding vehicle’s risk level, which is hard to evaluate. In this paper, a two-stage fuzzy inference model is constructed to evaluate a vehicle’s risk level, while a congestion control algorithm DRG-DCC (Driving Risk Game-Distributed Congestion Control) is proposed. Moreover, HPSO is employed to find optimal solutions. The simulation results show that the proposed method adjusts the transmission frequency based on driving risk, effectively striking a balance between transmission delay and channel busy rate.

Keywords

Distributed congestion control; fuzzy inference; driving risk evaluation; game theory; Nash equilibrium

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