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

A Vehicular Networking Perspective on Estimating Vehicle Collision Probability at Intersections

Abstract-Finding viable metrics to assess the effectiveness of intelligent transportation systems (ITSs) in terms of safety is one of the major challenges in vehicular networking research. We aim to provide a metric, i.e., an estimation of the vehicle collision probability at intersections, that can be used for evaluating intervehicle communication (IVC) concepts. In the last years, the vehicular networking community reported in several studies that safety-enhancing protocols and applications cannot be evaluated based only on networking metrics such as delays and packet loss rates. We present an evaluation scheme that addresses this need by quantifying the probability of a future crash, depending on the situation in which a vehicle is receiving a beacon message [e.g., a cooperative awareness message (CAM) or a basic safety message (BSM)]. Thus, our criticality metric also allows for fully distributed situation assessment. We investigate the impact of safety messaging between cars approaching an intersection using a modified road traffic simulator that allows selected vehicles to disregard traffic rules. As a direct result, we show that simple beaconing is not as effective as anticipated in suburban environments. More profoundly, however, our simulation results reveal more details about the timeliness (regarding the criticality assessment) of beacon messages, and as such, they can be used to develop more sophisticated beaconing solutions. Index Terms-Vehicle safety, vehicular ad hoc networks, wireless communication

Power and Packet Rate Control for Vehicular Networks in Multi-Application Scenarios

Vehicular networks require vehicles to periodically transmit 1-hop broadcast packets in order to detect other vehicles in their local neighborhood. Many vehicular applications depend on the correct reception of these packets that are transmitted on a common control channel. Vehicles will actually be required to simultaneously execute multiple applications. The transmission of the broadcast packets should hence be configured to satisfy the requirements of all applications while controlling the channel load. This can be challenging when vehicles simultaneously run multiple applications, and each application has different requirements that vary with the vehicular context (e.g. speed and density). In this context, this paper proposes and evaluates different techniques to dynamically adapt the rate and power of 1-hop broadcast packets per vehicle in multi-application scenarios. The proposed techniques are designed to satisfy the requirements of multiple simultaneous applications and reduce the channel load. The evaluation shows that the proposed techniques significantly decrease the channel load, and can better satisfy the requirements of multiple applications compared to existing approaches, in particular the Message Handler specified in the SAE J2735 DSRC Message Set Dictionary

Detecting Non-Line of Sight to Prevent Accidents in Vehicular Ad hoc Networks

There are still many challenges in the field of VANETs that encouraged researchers to conduct further investigation in this field to meet these challenges. The issue pertaining to routing protocols such as delivering the warning messages to the vehicles facing Non-Line of Sight (NLOS) situations without causing the storm problem and channel contention, is regarded as a serious dilemma which is required to be tackled in VANET, especially in congested environments. This requires the designing of an efficient mechanism of routing protocol that can broadcast the warning messages from the emergency vehicles to the vehicles under NLOS, reducing the overhead and increasing the packet delivery ratio with a reduced time delay and channel utilisation. The main aim of this work is to develop the novel routing protocol for a high-density environment in VANET through utilisation of its high mobility features, aid of the sensors such as Global Positioning System (GPS) and Navigation System (NS). In this work, the cooperative approach has been used to develop the routing protocol called the Co-operative Volunteer Protocol (CVP), which uses volunteer vehicles to disseminate the warning message from the source to the target vehicle under NLOS issue; this also increases the packet delivery ratio, detection of NLOS and resolution of NLOS by delivering the warning message successfully to the vehicle under NLOS, thereby causing a direct impact on the reduction of collisions between vehicles in normal mode and emergency mode on the road near intersections or on highways. The cooperative approach adopted for warning message dissemination reduced the rebroadcast rate of messages, thereby decreasing significantly the storm issue and the channel contention. A novel architecture has been developed by utilising the concept of a Context-Aware System (CAS), which clarifies the OBU components and their interaction with each other in order to collect data and take the decisions based on the sensed circumstances. The proposed architecture has been divided into three main phases: sensing, processing and acting. The results obtained from the validation of the proposed CVP protocol using the simulator EstiNet under specific conditions and parameters showed that performance of the proposed protocol is better than that of the GRANT protocol with regard to several metrics such as packet delivery ratio, neighbourhood awareness, channel utilisation, overhead and latency. It is also successfully shown that the proposed CVP could detect the NLOS situation and solves it effectively and efficiently for both the intersection scenario in urban areas and the highway scenario

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

An overview of VANET vehicular networks

Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their vehicles. Smart automobiles have developed to make that time safer, more effective, more fun, pollution-free, and affordable. However, maintaining the optimum use of resources and addressing rising needs continues to be a challenge given the popularity of vehicle users and the growing diversity of requests for various services. As a result, VANET will require modernized working practices in the future. Modern intelligent transportation management and driver assistance systems are created using cutting-edge communication technology. Vehicular Ad-hoc networks promise to increase transportation effectiveness, accident prevention, and pedestrian comfort by allowing automobiles and road infrastructure to communicate entertainment and traffic information. By constructing thorough frameworks, workflow patterns, and update procedures, including block-chain, artificial intelligence, and SDN (Software Defined Networking), this paper addresses VANET-related technologies, future advances, and related challenges. An overview of the VANET upgrade solution is given in this document in order to handle potential future problems

AICP: Augmented Informative Cooperative Perception

Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and lightweight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.Peer reviewe

A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches

The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents, and air pollution. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communication, and dynamic adaptive technologies to make the existing road traffic management systems (TMSs) more efficient to cope with the aforementioned issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up-to-date review of the different technologies used in the different phases involved in a TMS and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities

Distributed Adaptation Techniques for Connected Vehicles

In this PhD dissertation, we propose distributed adaptation mechanisms for connected vehicles to deal with the connectivity challenges. To understand the system behavior of the solutions for connected vehicles, we first need to characterize the operational environment. Therefore, we devised a large scale fading model for various link types, including point-to-point vehicular communications and multi-hop connected vehicles. We explored two small scale fading models to define the characteristics of multi-hop connected vehicles. Taking our research into multi-hop connected vehicles one step further, we propose selective information relaying to avoid message congestion due to redundant messages received by the relay vehicle. Results show that the proposed mechanism reduces messaging load by up to 75% without sacrificing environmental awareness. Once we define the channel characteristics, we propose a distributed congestion control algorithm to solve the messaging overhead on the channels as the next research interest of this dissertation. We propose a combined transmit power and message rate adaptation for connected vehicles. The proposed algorithm increases the environmental awareness and achieves the application requirements by considering highly dynamic network characteristics. Both power and rate adaptation mechanisms are performed jointly to avoid one result affecting the other negatively. Results prove that the proposed algorithm can increase awareness by 20% while keeping the channel load and interference at almost the same level as well as improve the average message rate by 18%. As the last step of this dissertation, distributed cooperative dynamic spectrum access technique is proposed to solve the channel overhead and the limited resources issues. The adaptive energy detection threshold, which is used to decide whether the channel is busy, is optimized in this work by using a computationally efficient numerical approach. Each vehicle evaluates the available channels by voting on the information received from one-hop neighbors. An interdisciplinary approach referred to as entropy-based weighting is used for defining the neighbor credibility. Once the vehicle accesses the channel, we propose a decision mechanism for channel switching that is inspired by the optimal flower selection process employed by bumblebees foraging. Experimental results show that by using the proposed distributed cooperative spectrum sensing mechanism, spectrum detection error converges to zero