An empirical study of Unfairness and Oscillation in ETSI DCC

International audience—Performance of Vehicular Adhoc Networks (VANETs) in high node density situation has long been a major field of studies. Particular attention has been paid to the frequent exchange of Cooperative Awareness Messages (CAMs) on which many road safety applications rely. In the present paper, se focus on the European Telecommunications Standard Institute (ETSI) Decentralized Congestion Control (DCC) mechanism, particularly on the evaluation of its facility layers component when applied in the context of dense networks. For this purpose, a set of simulations has been conducted over several scenarios, considering rural highway and urban mobility in order to investigate unfairness and oscillation issues, and analyze the triggering factors. The experimental results show that the latest technical specification of the ETSI DCC presents a significant enhancement in terms of fairness. In contrast, the stability criterion leaves room for improvement as channel load measurement presents (i) considerable fluctuations when only the facility layer control is applied and (i.i) severe state oscillation when different DCC control methods are combined

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

230501

Cooperative Vehicular Platooning (Co-VP) is a paradigmatic example of a Cooperative Cyber-Physical System (Co-CPS), which holds the potential to vastly improve road safety by partially removing humans from the driving task. However, the challenges are substantial, as the domain involves several topics, such as control theory, communications, vehicle dynamics, security, and traffic engineering, that must be coupled to describe, develop and validate these systems of systems accurately. This work presents a comprehensive survey of significant and recent advances in Co-VP relevant fields. We start by overviewing the work on control strategies and underlying communication infrastructures, focusing on their interplay. We also address a fundamental concern by presenting a cyber-security overview regarding these systems. Furthermore, we present and compare the primary initiatives to test and validate those systems, including simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Finally, we highlight a few open challenges in the Co-VP domain. This work aims to provide a fundamental overview of highly relevant works on Co-VP topics, particularly by exposing their inter-dependencies, facilitating a guide that will support further developments in this challenging field.info:eu-repo/semantics/publishedVersio

Resource allocation and congestion control in vehicular ad-hoc networks through optimization algorithms and artificial intelligence

[SPA] Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. En los últimos años la creciente demanda de la industria del transporte junto con requisitos de seguridad cada vez más estrictos han promovido el rápido desarrollo de las comunicaciones vehiculares. Tales comunicaciones se basan en el intercambio de mensajes periódicos (beacons) que contienen información crítica de los vehículos. Esta difusión de información da origen a lo que comúnmente se denomina conciencia cooperativa, que permite ampliar las capacidades de numerosos sistemas de asistencia en carretera y las diferentes aplicaciones de seguridad. Ciertamente, la difusión de información entre vehículos es la base de la conducción autónoma y reduce drásticamente el riesgo de colisión y otros eventos indeseados. Sin embargo, es importante tener en cuenta que la carga agregada de los beacons transmitidos puede congestionar rápidamente el canal, comprometiendo la recepción de paquetes y, por lo tanto, poniendo en peligro las ventajas que ofrecen tales comunicaciones. Para garantizar la disponibilidad del canal tanto para la recepción correcta de mensajes de emergencia y de las mínimas balizas necesarias para satisfacer los requisitos de las aplicaciones de seguridad, una determinada fracción del canal debe de ser reservada. En la literatura relacionada, el control de la congestión se ha abordado mediante el ajuste de varios parámetros de transmisión (tasa de mensaje, potencia y tasa de bit), pero todavía existen numerosos desafíos por abordar. Por ejemplo, aunque los parámetros de transmisión suelen ajustarse individualmente debido a la simplicidad del problema de optimización, aquí se muestran las ventajas de ajustar varios parámetros de forma simultánea. En esta tesis, se propone el uso de diferentes algoritmos distribuidos que alcancen el nivel de congestión deseado sin requerir infraestructura ninguna en carretera. La primera parte de esta tesis aborda la asignación de la tasa de balizamiento mediante la maximización de la utilidad de red (NUM) y diferentes métricas de riesgo como el tiempo de colisión y la velocidad de la carretera de aviso. En la segunda parte, no solo se estudian diferentes combinaciones consistentes de parámetros, sino que también nos sumergimos en el paradigma de los algoritmos no cooperativos, en los que no se requiere información de los vehículos vecinos. El problema de control de la congestión es formulado como un Proceso de Decisión de Markov (MDP) y resuelto mediante técnicas de inteligencia artificial, más concretamente, mediante aprendizaje por refuerzo (RL). Se proponen diferentes soluciones que van desde simples métodos tabulares, adecuados para entornos discretos, como Q-learning, hasta funciones de aproximación más complejas adecuadas para espacios continuos, como SARSA basado en semi-gradiente o redes neuronales artificiales.[ENG] This doctoral dissertation has been presented in the form of thesis by publication. The ever-increasing growth of the transportation industry demands combined with new safety requirements has triggered the development of vehicular communications. These communications among vehicles are based on the exchange of periodical messages or beacons containing valuable information about vehicle state. This gives rise to the socalled cooperative awareness, which allows extending the capabilities of numerous driver assistance systems and safety applications. Disseminating information among vehicles certainly lessens the risk of collision and other undesired events. Nevertheless, the aggregated beaconing load can rapidly jam the channel, compromising packet reception, and therefore endangering the advantages offered by such communications. To guarantee the availability of the channel for emergency messages and the minimum beacons receptions that satisfy safety application requirements, a given fraction of the channel capacity should be available. This congestion control has been addressed by adjusting several transmission parameters but some challenges are still unresolved. Although these parameters are usually optimized individually because of the convexity of the optimization problem, we show the advantages of combining them. In this thesis, we propose the use of different distributed algorithms that reach the desired congestion level without explicitly requiring any costly infrastructure. The first part of this thesis addresses beaconing rate allocation. We propose several distributed solutions based on Network Utility Maximization (NUM) and different risk metrics such as time-to-collision and advisory road speed. In the second part, we not only study different combinations of well-coupled parameters but also dive into the paradigm of noncooperative algorithms, in which no information from neighboring vehicles or centralized infrastructure are required. We formulate the congestion control problem as a Markov Decision Process and solve it by means of different reinforcement learning techniques. In particular, we propose different solutions ranging from tabular methods suitable for simple and discrete environments, like Q-learning, to more complex functions approximations for continuous action-state spaces, such as Semi-gradient SARSA or artificial neural networks.Esta tesis doctoral se presenta bajo la modalidad de compendio de publicaciones. Está formada por estos seis artículos: 1. (j1) Aznar-Poveda, J., Egea-Lopez, E., Garcia-Sanchez, A. J., and Pavon-Mariño, P. (2019, October). Time-to-Collision-Based Awareness and Congestion Control for Vehicular Communications. IEEE Access, 7, 154192-154208. DOI: 10.1109/ACCESS.2019.2949131. 2. (c1) Aznar-Poveda, J., Egea-Lopez, E., and Garcia-Sanchez, A. J. (2020, May). Cooperative Awareness Message Dissemination in EN 302 637-2: An Adaptation for Winding Roads. IEEE 91st Vehicular Technology Conference (VTC2020-Spring) (pp. 1-5). IEEE. DOI: 10.1109/VTC2020-Spring48590.2020.9128815. 3. (c2) Aznar-Poveda, J., Egea-Lopez, E., Garcia-Sanchez, A. J., and Garcia-Haro, J. (2020, July). Advisory Speed Estimation for an Improved V2X Communications Awareness in Winding Roads. In 2020 22nd International Conference on Transparent Optical Networks (ICTON) (pp. 1-4). IEEE. DOI: 10.1109/ICTON51198.2020.9203478 4. (j2) Aznar-Poveda, J., Garcia-Sanchez, A. J., Egea-Lopez, E., and Garcia-Haro, J. (2021, January). MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETs. IEEE Access, 9, 10166-10178. DOI: 10.1109/ACCESS.2021.3050625 5. (j3) Aznar-Poveda, J., Garcia-Sanchez, A. J., Egea-Lopez, E., and Garcia-Haro, J. (2021, August). Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach. IEEE Access 9, 122067-122081. DOI: 10.1109/ACCESS.2021.3109422 6. (j4) Aznar-Poveda, J., Garcia-Sanchez, A. J., Egea-Lopez, E., and Garcia-Haro, J. (2021, December). Approximate Reinforcement Learning to Control Beaconing Congestion in Distributed Networks. Scientific Reports, 12, 142. DOI: 10.1038/s41598-021-04123-9Escuela Internacional de Doctorado de la Universidad Politécnica de CartagenaUniversidad Politécnica de CartagenaPrograma de Doctorado en Tecnologías de la Información y las Comunicacione

Rate-Adaptation Based Congestion Control for Vehicle Safety Communications

This thesis deals with the scalability of Vehicle Safety Communications (VSC), where vehicles exchange periodic status messages to support future driver assistance applications. We systematically develop a design methodology for congestion control in VSC and present a resulting protocol named PULSAR. While previous works typically focused on controlling channel load only, we thereby integrate a concept which allows the adaptation to operate within the limits defined by safety applications

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks