Vehicular communication systems

About: Vehicular communication systems is a research topic. Over the lifetime, 2532 publications have been published within this topic receiving 64775 citations. The topic is also known as: V2V & vehicle-to-vehicle.

HINTS: A novel approach for realistic simulations of vehicular communications

Abstract: One of the main challenges in the Vehicular Ad-Hoc Networks (VANETs) research domain is the simulation of vehicular communications using realistic mobility models. Several efforts have been put lately in this purpose; yet, the proposed models are either inappropriate, or carry significant disadvantages. In this paper, we propose a novel approach for realistic simulations in vehicular networks, inspired from the hierarchical video and image compression technique. We developed HINTS (Hybrid Integration of Network and Traffic Simulators), the correspondent platform, engendered from the integration of SUMO, a traffic simulator and NS-3, a network simulator after adding some supplemental modules to both simulators. Its efficiency and performance are demonstrated in a proof-of-concept study, based on a realistic traffic management scenario.

Identity-based security systems for vehicular ad-hoc networks

Abstract: Cooperative Intelligent Transport Systems (ITS) based on vehicular car to car ad-hoc networks have been extensively investigated by the research community and industry to improve efficiency and safety in road traffic. The management and control of the vehicular ad-hoc network (VANET) is still one of the most challenging research fields in the networking domain. In particular, security and privacy protection are very important requirements for the design of VANETs. The potential high dynamicity of VANETs and the need for flexibility and scalability in ITS applications supports the research for new security frameworks and the application of novel cryptographic schemes that ensure authentication, integrity and confidentiality given the constrained computational environment in which such applications usually operate. This paper investigates the application of identity based (id-based, for short) cryptographic (IBC) scheme (in particular, signature schemes) to provide better security and privacy for VANET. Along with a presentation of the state-of-the-art in this area, this paper presents a security framework for car-to-car VANETs based on a protocol for the distributed generation of signing keys that overcome key escrow issues.

Securing mmWave Vehicular Communication Links with Multiple Transmit Antennas

Abstract: This paper presents a low-complexity physical layer security technique for millimeter wave (mmWave) vehicular communication systems. The proposed technique exploits the large dimensional antenna arrays available in mmWave systems and the road reflected path to generate location dependent transmission. This results in coherent transmission to the legitimate receiver and interference that jams eavesdroppers with sensitive receivers along the same lane of travel. Theoretical and simulation results demonstrate the validity and effectiveness of the proposed technique and show that high secrecy throughput can be achieved when compared to directional array transmission techniques.

Design of roadside communication infrastructure with QoS guarantees

Abstract: There are several kinds of envisioned vehicular applications: video delivery, accidents detection, dissemination of traffic announcements, and so forth. Such applications demand minimal (and possibly distinct) QoS guarantees that must couple the vehicular network. Given that vehicular networks will soon become reality, we demand strategies for planning and managing such networks. In this work we propose Delta (A), a QoS-based strategy for planning the roadside infrastructure supporting a vehicular network. Thus, the network provider may employ our strategy to design a new network, compare the performance of distinct vehicular networks, and even evaluate the adherence between vehicular applications and the network. Delta is based on two metrics: i) connectivity duration, and ii) percentage of vehicles presenting such connectivity duration. For instance, if a given vehicular application requires that 20% of the vehicles are connected during 30% of the trip, we say that such application requires a deployment Delta (0.3, 0.2). Complementary, we also present Delta-g, a greedy heuristic for solving Delta. A deployment Delta (0.1, 0.1) requires the coverage of 0.09% of the road network, while a deployment Delta (0.9, 0.9) requires 21.67% of coverage.

Multi-Agent RL Enables Decentralized Spectrum Access in Vehicular Networks

Abstract: In this paper, we investigate the joint sub-channel and power allocation problem for cellular vehicle-to-everything (V2X) communications, where multiple vehicle-to-infrastructure (V2I) users share the spectrum resources with vehicle-to-vehicle (V2V) users. In particular, a novel channel state information (CSI)-independent decentralized algorithm based on multi-agent reinforcement learning (MARL) is proposed to maximize the sum throughput of V2I links while meeting the latency and reliability requirements of V2V links. Specifically, we implement the individual double dueling deep recurrent Q-networks (D3RQN) and the carefully designed common reward to train the implicitly collaborative agents, through which, each agent optimizes the policy individually based solely on local CSI-independent observations. To handle the non-stationarity induced by multi-agent concurrent learning, we incorporate hysteretic Q-learning and concurrent experience replay trajectory (CERT) to stabilize the training process. Besides, we incorporate the approximate regretted reward (ARR) to alleviate the unstable reward estimation problem caused by shifting environment dynamics. Simulation results demonstrate that the proposed algorithm outperforms the baselines and can achieve close performance compared with the centralized Brute-force method. Furthermore, the proposed CSI-independent design exhibits comparable performance as the CSI-involved version, which sheds some light on the potential to further reduce the signalling overhead of machine learning-based vehicular communication systems.