Vehicle to Vehicle Communication System for Smart Cities

A Vehicle-to-Vehicle (V2V) communication system for smart cities is proposed here. The V2V communication system is an advance wireless technology to reduce the number of fatal roadway accidents by providing early warning messages. For development of smart cities V2V and V2R are important to reduce road accidents on highways. It gives ease of access by providing different facilities such as ATM transaction, accidents safety messages to the transport or central unit. Based on a careful analysis of application requirements, an effective protocol can be used, which comprising congestion control policies, service differentiation mechanisms and methods for emergency warning dissemination. The proposed protocol achieves low latency in delivering emergency warnings and use of efficient bandwidth in stressful road scenarios. This system uses WSN for communication between two vehicle modules

Vehicle to vehicle (V2V) wireless communications

This work focuses on the vehicle-to-vehicle (V2V) communication, its current challenges, future perspective and possible improvement.V2V communication is characterized by the dynamic environment, high mobility, nonpredective scenario, propagation effects, and also communicating antenna's positions. This peculiarity of V2V wireless communication makes channel modelling and the vehicular propagation quite challenging. In this work, firstly we studied the present context of V2V communication also known as Vehicular Ad-hoc Netwok (VANET) including ongoing researches and studies particularly related to Dedicated Short Range Communication (DSRC), specifically designed for automotive uses with corresponding set of protocols and standards. Secondly, we focused on communication models and improvement of these models to make them more suitable, reliable and efficient for the V2V environment. As specifies the standard, OFDM is used in V2V communication, Adaptable OFDM transceiver was designed. Some parameters as performance analytics are used to compare the improvement with the actual situation. For the enhancement of physical layer of V2V communication, this work is focused in the study of MIMO channel instead of SISO. In the designed transceiver both SISO and MIMO were implemented and studied successfully

Trust in Vehicle-to-Vehicle Communication

In traditional Pedestrian Automatic Emergency Braking (PAEB) system, vehicles equipped with onboard sensors such as radar, camera, and infrared detect pedestrians, alert the driver and/ or automatically take actions to prevent vehicle-pedestrian collision. In some situations, a vehicle may not be able to detect a pedestrian due to blind spots. Such a vehicle could benefit from the sensor data from neighboring vehicles in making such safety critical decisions. We propose a trust model for ensuring shared data are valid and trustworthy for use in making safety critical decisions. Simulation results of the proposed trust model show promise

A predefined channel coefficients library for vehicle-to-vehicle communications

It is noticeable that most of VANETs communications tests are assessed through simulation. In a majority of simulation results, the physical layer is often affected by an apparent lack of realism. Therefore, vehicular channel model has become a critical issue in the field of intelligent transport systems (ITS). To overcome the lack of realism problem, a more robust channel model is needed to reflect the reality. This paper provides an open access, predefined channel coefficients library. The library is based on 2x2 and 4x4 Multiple – Input – Multiple – Output (MIMO) systems in V2V communications, using a spatial channel model extended SCME which will help to reduce the overall simulation time. In addition, it provides a more realistic channel model for V2V communications; considering: over ranges of speeds, distances, multipath signals, sub-path signals, different angle of arrivals, different angle departures, no line of sight and line of sight. An intensive evaluation process has taken place to validate the library and acceptance results are produced. Having an open access predefined library, enables the researcher at relevant communities to test and evaluate several complicated vehicular communications scenarios in a wider manners with less time and efforts

Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications

Caching popular contents in vehicle-to-vehicle (V2V) communication networks is expected to play an important role in road traffic management, the realization of intelligent transportation systems (ITSs), and the delivery of multimedia content across vehicles. However, for effective caching, the network must dynamically choose the optimal set of cars that will cache popular content and disseminate it in the entire network. However, most of the existing prior art on V2V caching is restricted to cache placement that is solely based on location and user demands and does not account for the large-scale spatio-temporal variations in V2V communication networks. In contrast, in this paper, a novel spatio-temporal caching strategy is proposed based on the notion of temporal graph motifs that can capture spatio-temporal communication patterns in V2V networks. It is shown that, by identifying such V2V motifs, the network can find sub-optimal content placement strategies for effective content dissemination across a vehicular network. Simulation results using real traces from the city of Cologne show that the proposed approach can increase the average data rate by $45\%$ for different network scenarios

Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

Optimal Content Downloading in Vehicular Networks

We consider a system where users aboard communication-enabled vehicles are interested in downloading different contents from Internet-based servers. This scenario captures many of the infotainment services that vehicular communication is envisioned to enable, including news reporting, navigation maps and software updating, or multimedia file downloading. In this paper, we outline the performance limits of such a vehicular content downloading system by modelling the downloading process as an optimization problem, and maximizing the overall system throughput. Our approach allows us to investigate the impact of different factors, such as the roadside infrastructure deployment, the vehicle-to-vehicle relaying, and the penetration rate of the communication technology, even in presence of large instances of the problem. Results highlight the existence of two operational regimes at different penetration rates and the importance of an efficient, yet 2-hop constrained, vehicle-to-vehicle relaying

A Measurement Based Shadow Fading Model for Vehicle-to-Vehicle Network Simulations

The vehicle-to-vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for vehicular ad hoc networks (VANETs). Extensive research efforts have been made to develop V2V channel models to be implemented in advanced VANET system simulators for performance evaluation. The impact of shadowing caused by other vehicles has, however, largely been neglected in most of the models, as well as in the system simulations. In this paper we present a shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios. The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight due to buildings, with the help of video information recorded during the measurements. It is observed that vehicles obstructing the LOS induce an additional average attenuation of about 10 dB in the received signal power. An approach to incorporate the LOS/OLOS model into existing VANET simulators is also provided. Finally, system level VANET simulation results are presented, showing the difference between the LOS/OLOS model and a channel model based on Nakagami-m fading.Comment: 10 pages, 12 figures, submitted to Hindawi International Journal of Antennas and Propagatio