Survey on decentralized congestion control methods for vehicular communication

Vehicular communications have grown in interest over the years and are nowadays recognized as a pillar for the Intelligent Transportation Systems (ITSs) in order to ensure an efficient management of the road traffic and to achieve a reduction in the number of traffic accidents. To support the safety applications, both the ETSI ITS-G5 and IEEE 1609 standard families require each vehicle to deliver periodic awareness messages throughout the neighborhood. As the vehicles density grows, the scenario dynamics may require a high message exchange that can easily lead to a radio channel congestion issue and then to a degradation on safety critical services. ETSI has defined a Decentralized Congestion Control (DCC) mechanism to mitigate the channel congestion acting on the transmission parameters (i.e., message rate, transmit power and data-rate) with performances that vary according to the specific algorithm. In this paper, a review of the DCC standardization activities is proposed as well as an analysis of the existing methods and algorithms for the congestion mitigation. Also, some applied machine learning techniques for DCC are addressed

Adaptive Transmission Power with Vehicle Density for Congestion Control

The Intelligent Transport Systems (ITS) employs the Vehicular Ad-hoc Networks (VANET) technology to prevent and reduce accidents on highways. VANET uses wireless communication technology that includes protocols and applications that provides safety and non-safety features for a safe and comfortable driving experience. A major problem with VANET is that the network channel utilized for the transmission of network packets for awareness becomes congested due to vehicles competing to use the channel leading to packet loss, high transmission delay and unfair resource usage. These problems would eventually lead to the periodic exchange of Basic Safety Messages not being delivered on time, thereby making VANET unreliable. Researchers have focused on numerous approaches for controlling congestion on the network channel such as adapting the rate of transmission of packets i.e. the number of packets that can be sent per second or adjusting the transmission power which is the distance a packet can travel. An approach is proposed in this thesis to adapt the transmission power, based on the vehicle density state of the network, with the aim of reducing congestion on the network channel and improving the performance of VANET. Results indicate that this can lead to improved performance in terms of reduced packet loss and inter-packet delay

Design of an adaptive congestion control protocol for reliable vehicle safety communication

[no abstract

Adaptive Transmission Power Level with Vehicle Speed Approximation of Density for VANET Congestion Control

Vehicles travelling and communicating with each other and infrastructure is the basis of the future of vehicular transportation. There are many possible applications of communication in a vehicular network. One of the more important applications is for safety. Safety messages exchanged between vehicles can possibly be life-saving. However, if such messages are not received in a timely or reliable manner, a safety application’s effectiveness could suffer. As such, network congestion control is a popular topic in vehicular networks. Various methods of controlling the message transmission rate and power have been explored to-date. In this thesis we propose an algorithm which manipulates the transmission power based on a density estimation derived from the vehicle’s driving speed, and compare it to methods observing only speed, only density, or other factors. Analysis of the results was done through simulation software. Results showed that the proposed algorithm reduced symptoms of channel congestion at least as effectively as the related density-based algorithm, and much better than using no congestion control algorithm at all. This thesis also adds “relevance” as a new measurement of performance by observing the proportion of packets received from certain distances at each vehicle

Decentralized Congestion Control Algorithm for Vehicle to Vehicle Networks Using Oscillating Transmission Power

Wireless access in vehicular environments (WAVE) is a vehicle to vehicle (V2V) communications technology which could help prevent up to 82% of non-impaired accidents, according to the US DOT. A 2013 study by the World Health Organization estimated 2,227 road fatalities in 2009 alone. Currently the channel that is responsible for a vehicle’s awareness of others suffers from congestion at moderate loads. In this paper we propose a novel method for adjusting the transmission power in a pattern which alternates between high and low powered transmissions. We modify one commonly used decentralized congestion control (DCC) algorithm, LIMERIC, and compare the power adaptation model against two controls. WAVE supports a 300 meter transmission radius, however, less than 200 vehicles can communicate at the target rate of 10 transmissions per second. We demonstrate that our algorithm reduces the number of packets received by distant vehicles, while maintaining a higher packet rate to the closer vehicles, for which a higher rate is more important

Strengths and Weaknesses of the ETSI Adaptive DCC Algorithm: A Proposal for Improvement

This letter studies the adaptive decentralized congestion control (DCC) algorithm defined in the ETSI TS 102 687 V1.2.1 specification. We provide insights on the parameters used in the algorithm and explore the impact of those parameters on its performance. We show how the algorithm achieves good average medium utilization while protecting against congestion, but we also show how the chosen parameters can result in slow speed of convergence and long periods of unfairness in transitory situations. Finally, we propose a modification to the algorithm which results in significant improvements in the speed of convergence and fairness.This work was partially supported by the Spanish Ministerio de Economía y Competitividad through the Texeo project (TEC2016-80339-R)

Transmission Data Rate Control based Mechanism for Congestion Control in Vehicular Ad Hoc Networks (VANET)

Vehicular Ad Hoc Networks (VANET) supporting Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (v2I) communication can increase the efficiency and safety of the road transportation systems. VANET typically uses wireless communication technology and in scenarios with high vehicle densities, the communication channel faces congestion, negatively impacting the reliability of the safety applications. To prevent this, the European Telecommunication Standards Institute (ETSI) has proposed the Decentralized Congestion Control (DCC) methodology to effectively control the channel load, by controlling various message transmission parameters like message rate, data rate, and transmission power. Currently, most research works focus on the transmission power to control congestion, while the other approaches such as data rate and message rate control are less common. In this research, a data rate control algorithm has been proposed to control the network congestion based on the Channel Busy Ratio (CBR). For the simulations, real-world scenarios generated throughSUMO are considered. After comparing the results with other data rate control algorithms, the proposed approach is anticipated to perform better in the scenarios where the CBR is dynamic and high

Quality of Service in Vehicular Ad Hoc Networks: Methodical Evaluation and Enhancements for ITS-G5

After many formative years, the ad hoc wireless communication between vehicles has become a vehicular technology available in mass production cars in 2020. Vehicles form spontaneous Vehicular Ad Hoc Networks (VANETs), which enable communication whenever vehicles are nearby without need for supportive infrastructure. In Europe, this communication is standardised comprehensively as Intelligent Transport Systems in the 5.9 GHz band (ITS-G5). This thesis centres around Quality of Service (QoS) in these VANETs based on ITS-G5 technology. Whilst only a few vehicles communicate, radio resources are plenty, and channel congestion is a minor issue. With progressing deployment, congestion control becomes crucial to preserve QoS by preventing high latencies or foiled information dissemination. The developed VANET simulation model, featuring an elaborated ITS-G5 protocol stack, allows investigation of QoS methodically. It also considers the characteristics of ITS-G5 radios such as the signal attenuation in vehicular environments and the capture effect by receivers. Backed by this simulation model, several enhancements for ITS-G5 are proposed to control congestion reliably and thus ensure QoS for its applications. Modifications at the GeoNetworking (GN) protocol prevent massive packet occurrences in a short time and hence congestion. Glow Forwarding is introduced as GN extension to distribute delay-tolerant information. The revised Decentralized Congestion Control (DCC) cross-layer supports low-latency transmission of event-triggered, periodic and relayed packets. DCC triggers periodic services and manages a shared duty cycle budget dedicated to packet forwarding for this purpose. Evaluation in large-scale networks reveals that this enhanced ITS-G5 system can reliably reduce the information age of periodically sent messages. The forwarding budget virtually eliminates the starvation of multi-hop packets and still avoids congestion caused by excessive forwarding. The presented enhancements thus pave the way to scale up VANETs for wide-spread deployment and future applications

Approximate reinforcement learning to control beaconing congestion in distributed networks

In vehicular communications, the increase of the channel load caused by excessive periodical messages (beacons) is an important aspect which must be controlled to ensure the appropriate operation of safety applications and driver-assistance systems. To date, the majority of congestion control solutions involve including additional information in the payload of the messages transmitted, which may jeopardize the appropriate operation of these control solutions when channel conditions are unfavorable, provoking packet losses. This study exploits the advantages of non-cooperative, distributed beaconing allocation, in which vehicles operate independently without requiring any costly road infrastructure. In particular, we formulate the beaconing rate control problem as a Markov Decision Process and solve it using approximate reinforcement learning to carry out optimal actions. Results obtained were compared with other traditional solutions, revealing that our approach, called SSFA, is able to keep a certain fraction of the channel capacity available, which guarantees the delivery of emergency-related notifications with faster convergence than other proposals. Moreover, good performance was obtained in terms of packet delivery and collision ratios.This research has been supported by the projects AIM, ref. TEC2016-76465-C2-1-R, ARISE2 “Future IoT Networks and Nano-networks (FINe)” ref. PID2020-116329GB-C22, ONOFRE-3, ref. PID2020-112675RB-C41 [Agencia Estatal de Investigación (AEI), European Regional Development Fund (FEDER), European Union (EU)], ATENTO, ref. 20889/PI/18 (Fundación Séneca, Región de Murcia), and LIFE [Fondo SUPERA Covid-19, funded by Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC), Universidades Españolas and Banco Santander]. J.A.P. thanks the Spanish MECD for an FPI grant ref. BES-2017-081061. Finally, the authors acknowledge Laura Wettersten for her contribution in reviewing the grammar and spell of the manuscript