Efficient medium access control protocol for vehicular ad-hoc networks

Intelligent transportation systems (ITS) have enjoyed a tremendous growth in the last decade and the advancement in communication technologies has played a big role behind the success of ITS. Inter-vehicle communication (IVC) is a critical requirement for ITS and due to the nature of communication, vehicular ad-hoc network technology (VANET) is the most suitable communication technology for inter-vehicle communications. In Practice, however, VANET poses some extreme challenges including dropping out of connections as the moving vehicle moves out of the coverage range, joining of new nodes moving at high speeds, dynamic change in topology and connectivity, time variability of signal strength, throughput and time delay. One of the most challenging issues facing vehicular networks lies in the design of efficient resource management schemes, due to the mobile nature of nodes, delay constraints for safety applications and interference. The main application of VANET in ITS lies in the exchange of safety messages between nodes. Moreover, as the wireless access in vehicular environment (WAVE) moves closer to reality, management of these networks is of increasing concern for ITS designers and other stakeholder groups. As such, management of resources plays a significant role in VANET and ITS. For resource management in VANET, a medium access control protocol is used, which makes sure that limited resources are distributed efficiently. In this thesis, an efficient Multichannel Cognitive MAC (MCM) is developed, which assesses the quality of channel prior to transmission. MCM employs dynamic channel allocation and negotiation algorithms to achieve a significant improvement in channel utilisation, system reliability, and delay constraints while simultaneously addressing Quality of Service. Moreover, modified access priority parameters and safety message acknowledgments will be used to improve the reliability of safety messages. The proposed protocols are implemented using network simulation tools. Extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANET technologies. Finally, improvements in delay and packet delivery ratios are presented

Intelligent Traffic Monitoring System Using Vehicular Ad Hoc Network

The growing significance of road safety and human engagement in transport has emerged as a matter of national concern, exerting a profound impact on the lives of individuals.. Many road accidents and crashes failed to ensure human life safety. As a result, the traffic management system must maintain the balance in accordance with the maximum road limits. Vehicles with sensors and automated self-driving capabilities are now available, such as Tesla and others. The proposed system is based on a technique known as Intervention linear minimum spanning tree (ILMST), which employs a topology with lengths that are proportionally equal. When using dynamic topology, there is packet loss during a change of location or a continuous update in networking via vehicle movement from one location to another. In this manner, each node computes the weighted nodes with a number of partitions in order to provide a linear time update. This reduces the number of connected edges in the graph that are repeated. When the size of the repeated graphs that relate the GPS route from the maps is reduced, traffic updates avoid recursion and provide the best routes for customers. Traffic congestion overhead can be reduced by implementing the proposed methodology. It is possible to avoid it where there are traffic signals and all other sensor-based wireless devices in a vehicular Ad Hoc Network (VANET). The safety measures are also a necessary step based on the communications in routing and other protocols. The system, when combined with a neural network-based positioning system (NNPS) with various perceptrons, can maintain vehicle speed and categorize safety threats such as group classification. A solution can be found by repairing the DDoS attack based on the results of the various aspects that provide output for malicious monitoring

The Trap Coverage Area Protocol for Scalable Vehicular Target Tracking

Vehicle target tracking is a sub-field of increasing and increasing interest in the vehicular networking research area, in particular for its potential application in dense urban areas with low associated costs, e.g., by exploiting existing monitoring infrastructures and cooperative collaboration of regular vehicles. Inspired by the concept of trap coverage area, we have originally designed and implemented an original protocol for vehicle tracking in wide-scale urban scenarios, called TCAP. TCAP is capable of achieving the needed performance while exploiting a limited number of inexpensive sensors (e.g., public-authority cameras already installed at intersections for traffic monitoring), and opportunistic vehicle collaboration, with high scalability and low overhead if compared with state-of-the-art literature. In particular, the wide set of reported results show i) the suitability of our TCAP tracking in the challenging urban conditions of high density of vehicles, ii) the very weak dependency of TCAP performance from topology changes/constraints (e.g., street lengths and speed limits), iii) the TCAP capability of self-adapting to differentiated runtime conditions

Fog Connectivity Clustering and MDP Modeling for Software-defined Vehicular Networks

Intelligent and networked vehicles cooperate to create a mobile Cloud through vehicular Fog computing (VFC). Such clouds rely heavily on the underlying vehicular networks, so estimating communication resilience allows to address the problems caused by intermittent vehicle connectivity for data transfers. Individually estimating the communication stability of vehicles, nevertheless, undergoes incorrect predictions due to their particular mobility patterns. Therefore, we provide a region-oriented fog management model based on the connectivity through vehicular heterogeneous network environment via V2X and C-V2X. A fog management strategy dynamically monitors nearby vehicles to determine distinct regions in urban centres. The model enables a software-defined vehicular network (\Gls{SDVN}) controller to coordinate data flows. The vehicular connectivity described by our model assesses the potential for vehicle communication and conducts dynamic vehicle clustering. From the stochasticity of the environment, our model is based on Markov Decision Process (MDP), tracking the status of vehicle clusters and their potential for provisioning services. The model for vehicular clustering is supported by 5G and DSRC heterogeneous networks. Simulated analyses have shown the capability of our proposed model to estimate cluster reliability in real-time urban scenarios and support effective vehicular fog management

ON THE INTEGRATION OF VEHICULAR AD-HOC NETWORKS AND VISION-BASED DRIVER ASSISTANCE

Vehicular ad-hoc networks (VANETs) allow for short range wireless communication to share information between vehicles. Vision-based driver assistance (VBDA) uses computer vision to obtain information about nearby objects. The goal of both systems is to create a model of the environment surrounding the vehicle in order to make decisions. With unique strengths and weaknesses the two systems complement each other well. A simulation environment for both VANETs and VBDA is created to test both systems alongside one another. They are evaluated and then combined to build the best possible model of the environment with the goal of improving vehicle safety under adverse condition

An efficient cluster-based service model for vehicular ad-hoc networks on motorways

Vehicular Ad-Hoc Networks (VANET) can, but not limited to provide users with useful traffic and environmental information services to improve travelling efficiency and road safety. The communications systems used in VANET include vehicle-to-vehicle communications (V2V) and vehicle-to-infrastructure communications (V2I). The transmission delay and the energy consumption cost for maintaining good-quality communications vary depending on the transmission distance and transmission power, especially on motorways where vehicles are moving at higher speeds. In addition, in modern transportation systems, electric vehicles are becoming more and more popular, which require a more efficient battery management, this also call for an efficient way of vehicular transmission. In this project, a cluster-based two-way data service model to provide real-time data services for vehicles on motorways is designed. The design promotes efficient cooperation between V2V and V2I, or namely V2X, with the objective of improving both service and energy performance for vehicular networks with traffic in the same direction. Clustering is an effective way of applying V2X in VANET systems, where the cluster head will take the main responsibility of exchanging data with Road Side Units (RSU) and other cluster members. The model includes local service data collection, data aggregation, and service data downloading. We use SUMO and OMNET++ to simulate the traffic scenarios and the network communications. Two different models (V2X and V2I) are compared to evaluate the performance of the proposed model under different flow speeds. From the results, we conclude that the cluster-based service model outperforms the non-clustered model in terms of service successful ratio, network throughput and energy consumption