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

Abstract

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.

Distributed rate and power control in DSRC

“…A distributed algorithm was proposed to adjust the rate with the objective to maximize the utility function. Similarly, [15] also provided a NUM formulation on the rate control problem and proposed a fair adaptive beaconing rate for intervehicular communications (FABRIC) algorithm, which essentially is a particular scaled gradient projection algorithm to solve the dual of the NUM problem.…”

MAC2: Enabling multicasting and congestion control with multichannel transmission for intelligent vehicle terminal in Internet of Vehicles

“…To provide DSRC service, both American and European standards have adopted wireless access in vehicular environments (WAVE) or IEEE 802.11p as PHY and MAC layers via carrier sense multiple access with collision avoidance (CSMA/CA). [10][11][12] In WAVE, each vehicle periodically switches on control channel (CCH) to disseminate control and safety-related messages with high priority toward all surrounding vehicles through single-hop broadcasting and then tune into one of available service channels (SCHs) to exchange all the other low priority service messages. In this way, the joint use of CCH for safety-related messages and SCHs for service messages will necessitate the multichannel transmission in vehicular communications.…”

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

“…In [9], Yao et al derived a loss differentiation rate adaptation scheme to meet the stringent delay and reliability requirements for V2V safety communications. In [10], Egea-Lopez et al designed a fair adaptive beaconing rate algorithm for beaconing rate control in inter-vehicular communications.…”