Distributed and Fair Beaconing Rate Adaptation for Congestion Control in Vehicular Networks

TL;DR

This paper introduces FABRIC, a decentralized algorithm for fair and efficient beaconing rate control in vehicular networks, modeled as a Network Utility Maximization problem, ensuring convergence and fairness.

Contribution

It formulates beacon rate control as a NUM problem and proposes FABRIC, a scalable, decentralized algorithm with proven convergence for fair rate allocation.

Findings

FABRIC converges to fair beaconing rates in simulations.

The approach balances efficiency and fairness effectively.

Simulation results validate the algorithm's robustness in dynamic scenarios.

Abstract

Cooperative inter-vehicular applications rely on the exchange of broadcast single-hop status messages among vehicles, called beacons. The aggregated load on the wireless channel due to periodic beacons can prevent the transmission of other types of messages, what is called channel congestion due to beaconing activity. In this paper we approach the problem of controlling the beaconing rate on each vehicle by modeling it as a Network Utility Maximization (NUM) problem. This allows us to formally apply the notion of fairness of a beaconing rate allocation in vehicular networks and to control the trade-off between efficiency and fairness. The NUM methodology provides a rigorous framework to design a broad family of simple and decentralized algorithms, with proved convergence guarantees to a fair allocation solution. In this context, we focus exclusively in beaconing rate control and propose the Fair Adaptive Beaconing Rate for Intervehicular Communications (FABRIC) algorithm, which uses a particular scaled gradient projection algorithm to solve the dual of the NUM problem. The desired fairness notion in the allocation can be established with an algorithm parameter. Simulation results validate our approach and show that FABRIC converges to fair rate allocations in multi-hop and dynamic scenarios.