Decentralized Congestion Control Research Articles

Evaluation of Greedy and CBF for ETSI non-area GeoNetworking: The impact of DCC

This paper evaluates the performance of the two ETSI non-area forwarding algorithms in the GeoNetworking specification: Greedy Forwarding and Non-Area Contention-Based Forwarding (CBF). Non-area forwarding occurs when a packet is sent to a geographical Destination Area from a node located outside of this area, e.g., when a vehicle wants to alert of hazardous events to other vehicles located in a distant geographical area. The evaluation has been carried out both in urban and highway scenarios and takes into account the complete ETSI Architecture, including the interaction with the Decentralized Congestion Control (DCC) mechanism. We have also compared ETSI-defined mechanisms with optimizations found in the literature. Our main findings are that Greedy Forwarding, when combined with DCC, is extremely ineffective even with optimizations, and Non-Area CBFs (both ETSI CBF and an optimized version called S-FoT+) outperform Greedy Forwarding both in highway and urban scenarios.

Machine Learning-Based Risk-Aware Congestion Control Scheme for Minimization of Information Loss in Dense VANET Environment

Vehicular Ad-Hoc Network is one of the most growing research fields and has become a promising topic for researchers. Day by day, vehicle density increases, causing very serious issues like road accidents and traffic jams. These issues can be minimized by a periodic exchange of awareness messages among the vehicles. Traffic density increases transmission of awareness messages, which causes channel congestion and degrades safety services, so congestion control schemes must ensure that the channel load should be below a particular threshold, boost the quality of services and minimize information loss and delay in the network. This paper proposes a machine learning-based risk-aware congestion control (MLB-RACC) scheme which is an efficient congestion control scheme based on the modified [Formula: see text] -means machine learning algorithms. MLB-RACC technique processes the input data and helps to improve the quality of services in the automotive industry. It works in three phases: detecting congestion, clustering of vehicles and controlling the broadcasting of awareness messages for decreasing the channel load. MLB-RACC scheme works by grouping the nearest vehicles and the number of groups or clusters is decided on the basis of transmission range/transmission power. The group member selects adaptive transmission rate by looking at the channel busy ratio (CBR). In this technique, message generation rate is controlled at two levels: first is at the clustering level and second is during the checking of CBR. The scheme is implemented through MATLAB, NS2 and the SUMO platform and provides evidence for the minimization of information loss by analyzing the throughput, packet loss and end-to-end delay in comparison to ordinary decentralized congestion control (DCC) technique.

Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET

Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load.

Studying and improving the performance of ETSI ITS contention-based forwarding (CBF) in urban and highway scenarios: S-FoT+

This paper evaluates the performance of ETSI ITS Contention-Based Forwarding (CBF) and ETSI Simple GeoBroadcast forwarding while disseminating warning messages over a Geographical Area in highway and urban scenarios. Our experimental evaluation considers the complete ETSI ITS architecture including the Decentralized Congestion Control (DCC) mechanism. We propose an enhanced CBF mechanism, named S-FOT+, which combines several improvements to the ETSI CBF algorithm. S-FoT+ has a similar or better performance than the ETSI forwarding algorithms regarding both reliability and end-to-end delay while requiring much fewer transmissions. The improvements are equally effective and efficient in both urban and highway scenarios with large Destination Areas. Finally, we evaluate the trade-offs that stem from using multi-hop broadcast mechanisms in urban settings with smaller Destination Areas when compared to single-hop broadcast. Results show that multi-hop mechanisms significantly improve coverage at the cost of an increased number of transmissions.

Dynamic Beacon Distribution Mechanism for Internet of Vehicles: An Analytical Study

In the last decade, with the arrival of the 5G communication technology and the increasing numbers of vehicles being connected to the internet, conventional vehicle ad-hoc networks (VANETs) are evolving towards the internet of vehicles (IoV), which makes the co-existence of IEEE 802.11p and 5G-based technologies very important for the design of a heterogeneous IoV system that takes advantage of both. The IEEE 802.11p standard is still the best candidate to support direct communications for safety critical services. In fact, both the ETSI ITS-G5 and the IEEE 1609 standard families adopt the IEEE 802.11p standard as a medium access control (MAC) mechanism, and they require vehicles to exchange periodic awareness messages to avoid dangerous situations. When the density of vehicles increases, the MAC layer will suffer from radio channel congestion problems, and this may affect the various VANET applications, especially safety applications. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate the channel congestion; this was achieved by adapting the transmission parameters, such as the transmit power and data-rate. However, many research studies have demonstrated limitations and a low performance of DCC, especially when the channel load is extremely high. To deal with this, in this paper, we investigate a new promising technique, called the transmission timing control (TTC), to control the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time in order to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. The demonstrated results show the efficiency of our timing control-enabled scheme to deal with the channel load on top of different conditions.

Analysis of Congestion Control Mechanisms for Cooperative Awareness in IoV Environments

Traditional vehicle ad hoc networks (VANETs) have evolved toward the Internet of Vehicles (IoV) during the past ten years with the introduction of 5G communication technology and the growing number of vehicles linked to the Internet. The coexistence of IEEE 802.11p and 5G becomes critical to build a heterogeneous IoV system that benefits from both technologies, being that the IEEE 802.11p standard remains the best option for direct communications and safety-critical applications. The IEEE 1609 standard family and the ETSI ITS-G5 standard family both use the IEEE 802.11p standard as a MAC mechanism. To avoid dangerous situations, vehicles require the periodic exchange of awareness messages. With the increase in vehicle density, the MAC layer will suffer from radio channel congestion problems, which in turn will have a negative impact on the safety application requirements. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate channel congestion; this was achieved by adapting transmission parameters such as transmit power and data rate. However, several studies have demonstrated that DCC has drawbacks and suffers from poor performance when the channel load is very high. This paper investigates a new promising DCC technique called transmission timing control (TTC), to reduce the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. Obtained results showed a convergence between the applied experiments and our mathematical model, achieving an error margin of only 5%, which confirms the validity of the equation proposed.

Congestion Control in Autonomous Resource Selection of Cellular-V2X

Intelligent transportation systems have recently become a promising technology for future industry to provide safe, green, and automated driving. In this regard, the Third Generation Partnership Project (3GPP) has proposed to utilize cellular communication to enable direct communication between vehicles with and without cellular infrastructure assistance. 3GPP has introduced the Sensing-Based Semi-Persistent Scheduling (S-SPS) technique when coverage of the cellular system is absent. S-SPS faces resource collision and performance degradation problems when channel load increases in the network. This paper suggests a decentralized congestion control and transmission power control mechanism (TPC-DCC) with an adaptive threshold for the received signal as a combination method to decrease channel load in the network. Furthermore, this work introduces a novel channel load adjustment. The new adjusting algorithm is based on a constant difference between the upper and lower boundaries of channel load at each level to handle channel overload and provide more flexibility using DCC mechanisms. The interactions of the proposed algorithm with S-SPS and the Extended-Estimation Reservation Resource Allocation (E-ERRA) algorithms that the authors previously proposed are investigated. The results indicate that system performance can be substantially improved when the transmission power and reception threshold are adaptively adjusted to the proposed channel load adjustment. The results of E-ERRA with the proposed channel load adjustment method show promising results compared to S-SPS.

Evaluation and improvement of ETSI ITS Contention-Based Forwarding (CBF) of warning messages in highway scenarios

This paper evaluates the performance of the ETSI Contention-Based Forwarding (CBF) GeoNetworking protocol for distributing warning messages in highway scenarios, including its interaction with the Decentralized Congestion Control (DCC) mechanism. Several shortcomings of the standard ETSI CBF algorithm are identified, and we propose different solutions to these problems, which are able to reduce the number of transmissions by an order of magnitude, while reducing the message end-to-end delay and providing a reliability close to 100% in a large area of interest.

Multistate Active Combined Power and Message/Data Rate Adaptive Decentralized Congestion Control Mechanisms for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have emerged in time to reduce on-road fatalities and provide efficient information exchange for entertainment-related applications to users in a well-organized manner. VANETs are the most instrumental elements in the Internet of Things (IoT). The objective lies in connecting every vehicle to every other vehicle to improve the user’s quality of life. This aim of continuous connectivity and information exchange leads to the generation of more information in the medium, which could congest the medium to a larger extent. Decentralized congestion control (DCC) techniques are specified to reduce medium congestion and provide various safety applications. This article presents two DCC mechanisms that adapt message rate and data rate combined with transmit power control mechanism. These mechanisms are developed under multi-state active design proposed by the standard. The proposed methods deliver better performance over other mechanisms in terms of power, channel load, and channel utilization using real-time-based scenarios by simulation in SUMO.

Delay-Aware Multi-Layer Multi-Rate Model Predictive Control for Vehicle Platooning Under Message-Rate Congestion Control

Vehicle platooning is an enabler technology for increasing road capacity, improving safety and reducing fuel consumption. Platoon control is a two-layered system where each layer runs under a different communication standard and rate – (i) the upper-layer operates under a specific V2V communication standard such as IEEE 802.11p and (ii) the lower-layer operates over high-speed in-vehicle communication networks such as FlexRay, CAN. The upper-layer, under 802.11p, uses periodic Cooperative Awareness Messages (CAMs) for exchanging vehicle motion information (i.e., acceleration, velocity and so on), the rate of which is adapted depending on the network congestion level. With over 70% channel load, the CAMs experience significant delay and packet loss, jeopardizing the stability of the platoon control. Under such high congestion, the European Telecommunications Standard Institute (ETSI) proposes to engage Decentralized Congestion Control (DCC) to control the channel load. We propose a platoon control and DCC scheme to tackle this scenario. Our contribution is three-fold. First, we propose a multi-layer platoon model explicitly augmenting the communication delay in the state-space. Second, the augmented delay-aware platoon model is integrated in the state-of-the-art multi-layer multi-rate model predictive control (MPC) for the upper-layer. Third, we adopt a message-rate congestion control scheme to keep the channel load under a given threshold. We use the proposed delay-aware MPC scheme under the message-rate congestion control scheme which may lead to switching under dynamic network conditions. Using the proposed technique, we show that platoon performance can be maintained under high network congestion while maintaining string stability.

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

Channel congestion has been an open challenge for vehicular networks due to the limited resource of communication channels. Explosion of channel access requests from a massive number of transmitter vehicles can exhaust bandwidth and then degrade transmission quality. The rapid drop of messages (because of the high bit error rate in the transmission congestion condition) can threaten the safety of connected vehicles. Maintaining congestion-free communications is then essential to improve the reliability for vehicular networks, including Cellular-V2X (C-V2X)-based cooperative intelligent transport systems and road-safety applications. In this work, we present a novel intelligent transmission control model, namely DEEPCUT, to automatically adjust the message broadcasting rate of a transmitter vehicle. DEEPCUT works based on a Double Deep Q-learning Networks with Prioritized Experience Relay framework. DEEPCUT encourages the transmitter vehicle to (1) reduce its broadcasting rate if the vehicle is maintaining a safe distance from its neighbors and (2) increase the rate if the vehicle is approaching the others at a high-risk distance, all done by using reward/punish strategies. The evaluation results show that DEEPCUT can cut up 16% redundant data while increasing 22% packet reception rate compared with baseline models, particularly in crowd vehicular communications. Our risk-based transmission control can be an excellent complement to address the congestion when the channel cannot satisfy every vehicle’s resource requests. At best, the risk assessment-based approach in our congestion control method can provide a novel material to enhance Decentralized Congestion Control (DCC) for 5G V2X sidelink in the coming specifications.

A traffic‐centric fuzzy approach for solving the starvation problem of cooperative awareness messages in vehicular ad hoc networks

SummaryCongestion problem in vehicular ad hoc networks (VANETs) is one of the main factors that negatively affect the inter‐vehicle communication (IVC) efficiency of such emerging networks. Decentralized congestion control (DCC) is an effective method for overcoming the congestion problem. However, lower priority packets such as cooperative awareness messages (CAMs) may be encountered with the starvation problem in the DCC approach. This paper proposes FDCC: a traffic‐centric Fuzzy DCC approach for improving CAMs delivery ratio, especially in congested traffic flows. FDDC architecture consists of four fuzzy inference systems (FISs). The inter‐vehicle distance (IVD) and the speed variation are the spatiotemporal fuzzy inputs, which are customized for both single direction and multi‐direction scenarios. Based on the FIS outputs, the FDCC control system, unlike the DCC, provides a way for processing starved CAMs by ignoring unnecessary even‐driven messages. The obtained simulation results through OMNET++ in both single and multi‐direction scenarios show the efficiency of FDCC in terms of the end‐to‐end delay, network throughput, and packet delivery ratio for event‐driven messages as well as essential CAMs.

Balancing Awareness and Congestion in Vehicular Networks Using Variable Transmission Power

Vehicular ad Hoc networks (VANETs) support a variety of applications ranging from critical safety applications to “infotainment” or “comfort” applications. In North America, 75 MHz of the spectrum in the 5.9 GHz band has been allocated for vehicular communication. Safety applications rely on event-driven “alert” messages as well as the periodic broadcast of Basic Safety Messages (BSMs) containing critical information, e.g., position, speed, and heading from participating vehicles. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. In this paper, we propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage.

GoT: Decreasing DCC Queuing for CAM Messages

Vehicular networks use Decentralized Congestion Control (DCC) mechanisms to operate effectively, but this mechanism may introduce queuing delays. Freshness of Cooperative Awareness Messages (CAMs) is critical for their usefulness. In this letter we explore how the presence of other types of traffic additional to CAMs, even with lower priorities, has an impact on the freshness of CAM messages due to DCC queuing. Finally, we propose Generate-on-Time (GoT), which is a simple mechanism that reduces DCC queuing delays for CAM messages without introducing any downside in other performance metrics.

Experimental Evaluation of the ETSI DCC Adaptive Approach and Related Algorithms

Decentralized Congestion Control (DCC) mechanisms have been a core part of protocol stacks for vehicular networks since their inception and standardization. The ETSI ITS-G5 protocol stack for vehicular communications considers the usage of DCC not only in the network or access layers, but also as a part of the cross-layer architecture that influences how often messages are generated and transmitted. ETSI DCC mechanisms have evolved from a reactive approach based on a finite state machine, to an adaptive approach that relies on a linear control algorithm. This linear control algorithm, called LIMERIC, is the basis of the mechanism used in the ETSI DCC Adaptive Approach. The behavior of this algorithm depends on a set of parameters. Different values for these parameters have been proposed in the literature, including those defined in the ETSI specification. A recent proposal is Dual- $\alpha $ , which chooses parameters to improve convergence and fairness when the algorithm has to react to fast changes in the use of the shared medium (transitory situations). This article evaluates, by means of simulations, the performance of the ETSI DCC Adaptive Approach and related algorithms, considering both steady state and transitory situations. Results show that a bad selection of parameters can make a DCC algorithm ineffective, that the ETSI DCC Adaptive algorithm performs well in steady state conditions, and that Dual- $\alpha $ performs as well in steady state conditions and outperforms the ETSI DCC Adaptive Approach in transitory scenarios.

Cooperative Perception for Connected and Automated Vehicles: Evaluation and Impact of Congestion Control

Automated vehicles make use of multiple sensors to detect their surroundings. Sensors have significantly improved over the years but still face challenges due to the presence of obstacles or adverse weather conditions, among others. Cooperative or collective perception has been proposed to help mitigate these challenges through the exchange of sensor data among vehicles using V2X (Vehicle-to-Everything) communications. Recent studies have shown that cooperative perception can complement on-board sensors and increase the vehicle's awareness beyond its sensors field of view. However, cooperative perception significantly increases the amount of information exchanged by vehicles which can degrade the V2X communication performance and ultimately the effectiveness of cooperative perception. In this context, this study conducts first a dimensioning analysis to evaluate the impact of the sensors' characteristics and the market penetration rate on the operation and performance of cooperative perception. The study then investigates the impact of congestion control on cooperative perception using the Decentralized Congestion Control (DCC) framework defined by ETSI. The study demonstrates that congestion control can negatively impact the perception and latency of cooperative perception if not adequately configured. In this context, this study demonstrates for the first time that the combination of congestion control functions at the Access and Facilities layers can improve the perception achieved with cooperative perception and ensure a timely transmission of the information. The results obtained demonstrate the importance of an adequate configuration of DCC for the development of connected and automated vehicles.

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.

A Review on Congestion Control in Vehicular Ad Hoc Network at MAC Layer

Vehicular ad-hoc network (VANET) has been designed to improve traffic safety applications by enabling vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. In VANET, each and every vehicle exchanges the information among them, therefore, a high density of vehicles lead to the problem of congestion. In this context, Medium Access Control (MAC) protocols have been designed to improve the real-time communication between vehicles and roadside components with minimal transmission collision. Due to the VANET characteristics, maintaining communication at the MAC layer are the key challenges in collision control scheme. Therefore, it results in end-to-end delay and packet loss during communication. Towards the solution of these critical challenges, Decentralized Congestion control (DCC) mechanism has been innovated by the European Telecommunication Standard Institute (ETSI). This paper describes the various key challenges involved in congestion control. Also, the available solutions are described in details along with their limitations. This paper focuses on controlling of the channel load measured by DCC mechanism using different transmission parameters such as data transmission, rate transmission, power transmission and sensitive transmission.

Evaluation of Congestion-Enabled Forwarding With Mixed Data Traffic in Vehicular Communications

ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding ( CBF ) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node’s message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF , which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF , and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.

Age-of-Information-Aware Decentralized Congestion Control in VANETs

Decentralized Congestion Control (DCC) is one the central components of the inter-vehicular communications protocol stack enabling Cooperative Intelligent Transportation System (C-ITS). In this letter, we first present an analytical model for the evaluation of DCC in IEEE 802.11p/ITS-G5 vehicular networks. Then, we use it to tune the DCC algorithm specified by ETSI, and suggest two approaches which allow to not only avoid channel congestion, but also to meet C-ITS application requirements on age-of-information, what makes our approach practically relevant for safety critical C-ITS.