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.

MAC for Dedicated Short Range Communications in Intelligent Transport System

Abstract: In new intelligent transportation system of North American, the emphasis of the surface transportation infrastructure is based on vehicle-to-vehicle wireless communications,while 75 MHz dedicated short range communications may content to future delivery of rich media via vehicle-to-roadside links. This paper provides a tutorial overview of DSRC applications, assess IEEE 802.11 PHY and MAC layer characteristics, limitations of 802.11 in DSRC environments and state of DSRC. and anticipates that 802.11 specifications will need to be suitably altered to meet requirements for DSRC environments of multihop connectivity, high vehicle mobility and a variety of QOS requirements.

Towards low-latency and ultra-reliable vehicle-to-vehicle communication

Abstract: Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling technology to ensure traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation for V2V communication is proposed. The proposed approach exploits the spatial-temporal aspects of vehicles in terms of their physical proximity and traffic demands, to minimize the total transmission power while considering queuing latency and reliability. Due to the overhead caused by frequent information exchange between vehicles and the roadside unit (RSU), the centralized problem is decoupled into two interrelated subproblems. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles in zones based on their physical proximity. Given the vehicles' traffic demands and their QoS requirements, resource blocks are assigned to each zone. Second, leveraging techniques from Lyapunov stochastic optimization, a power minimization solution is proposed for each V2V pair within each zone. Simulation results for a Manhattan model have shown that the proposed scheme outperforms the baseline in terms of average queuing latency reduction up to 97% and significant improvement in reliability.

Throughput of Infrastructure-Based Cooperative Vehicular Networks

Abstract: In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.

DAGIoV: A Framework for Vehicle to Vehicle Communication Using Directed Acyclic Graph and Game Theory

Abstract: Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The Blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for microtransactions or frequent data transfer among the nodes in the vehicular network.

Intelligent traffic management at intersections supported by V2V and V2I communications

Abstract: This paper describes an intelligent traffic management system applied to road intersections, namely round-about and crossroads. A microscopic traffic simulator was developed to study intelligent traffic management techniques and evaluate their performance. The intelligent management techniques are aimed to minimize accidents, traffic congestion and consequently the environmental costs of road traffic. Each vehicle is modeled by an agent and each agent provides information depending on its vehicle sensors. Two intersection types, roundabout and crossroads, were simulated each using its intelligent traffic management system. Both intersections use an algorithm based on a spatio-temporal reservation scheme. The envisioned intelligent traffic management algorithm is supported by vehicle-to-vehicle and vehicle-to-infrastructure communications, allowing the exchange of information between vehicles and the intersection intelligent traffic management system. The developed intelligent traffic management system is very well suited for autonomous vehicles, it can also be used by human drivers if they follow accurately the proposed speed profile along the path.