A New Clustering Protocol for Hybrid Sensor Vehicular Networks (HSVNs)

As the UAE is classified among the highest countries in road accidents; it’s an urgent necessity to derive proper and effective solutions. One of the well-known solutions is to move to the Intelligent Transportation Systems (ITS). ITS is achieved by the implementation of advanced technologies to help the roads to be smarter, safer and more coordinated. There are different studies and researches published in this area. This thesis is mainly conducted to achieve the same goal, which is increasing the safety on our roads. The aim of this research is to build a new clustering protocol in the Hybrid Sensor Vehicular Networks (HSVN). HSVN is introduced as a new concept that integrates wireless sensor networks along with vehicular networks to increase the overall performance of both networks and the need of such new protocol in these networks is high. Although there are many published proposals, there is still a need to have a complete protocol that can reduce the shortcomings in the proposed solutions. The main features in the proposed protocol are creating a balance system by dividing the clustering process overheads between the vehicles in the network and the Road Side Unit (RSU), so instead of relying only on the vehicles’ resources, the RSU shares the process’s overhead by collecting data, calculating weighting factors, and electing suitable cluster heads. Moreover, the proposed protocol reduces the computational and the communication costs by electing two cluster heads for each cluster; one acts as the main cluster head and the other as a standby cluster head. By this feature, if a cluster head moves outside the cluster region, there is no need to run the clustering process again to elect a new cluster head as a standby cluster head exists. Also, one of the main characteristics of the new protocol is minimizing the collision in the system, and consequently increasing the throughput by defining the upper bound of the number of members in each cluster. The overall performance of the proposed protocol is very good and promises to solve many challenges in the existing protocols. In addition, the results show that this protocol outperforms one of the best existing mobility protocols in terms of the total number of clusters formed in the network, the number of single node clusters, and the saturation throughput of the clusters, the communication overheads and energy consumption reductions

Improving VANET Protocols via Network Science

Developing routing protocols for Vehicular Ad Hoc Networks (VANETs) is a significant challenge in these large, self- organized and distributed networks. We address this challenge by studying VANETs from a network science perspective to develop solutions that act locally but influence the network performance globally. More specifically, we look at snapshots from highway and urban VANETs of different sizes and vehicle densities, and study parameters such as the node degree distribution, the clustering coefficient and the average shortest path length, in order to better understand the networks' structure and compare it to structures commonly found in large real world networks such as small-world and scale-free networks. We then show how to use this information to improve existing VANET protocols. As an illustrative example, it is shown that, by adding new mechanisms that make use of this information, the overhead of the urban vehicular broadcasting (UV-CAST) protocol can be reduced substantially with no significant performance degradation.Comment: Proceedings of the 2012 IEEE Vehicular Networking Conference (VNC), Korea, November 201

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

The Dynamics of Vehicular Networks in Urban Environments

Vehicular Ad hoc NETworks (VANETs) have emerged as a platform to support intelligent inter-vehicle communication and improve traffic safety and performance. The road-constrained, high mobility of vehicles, their unbounded power source, and the emergence of roadside wireless infrastructures make VANETs a challenging research topic. A key to the development of protocols for inter-vehicle communication and services lies in the knowledge of the topological characteristics of the VANET communication graph. This paper explores the dynamics of VANETs in urban environments and investigates the impact of these findings in the design of VANET routing protocols. Using both real and realistic mobility traces, we study the networking shape of VANETs under different transmission and market penetration ranges. Given that a number of RSUs have to be deployed for disseminating information to vehicles in an urban area, we also study their impact on vehicular connectivity. Through extensive simulations we investigate the performance of VANET routing protocols by exploiting the knowledge of VANET graphs analysis.Comment: Revised our testbed with even more realistic mobility traces. Used the location of real Wi-Fi hotspots to simulate RSUs in our study. Used a larger, real mobility trace set, from taxis in Shanghai. Examine the implications of our findings in the design of VANET routing protocols by implementing in ns-3 two routing protocols (GPCR & VADD). Updated the bibliography section with new research work

Detecting Traffic Conditions Model Based On Clustering Nodes Situations In VANET

In the last decade, cooperative vehicular network has been one of the most studied areas for developing the intelligent transportation systems (ITS). It is considered as an important approach to share the periodic traffic situations over vehicular ad hoc networks (VANETs) to improve efficiency and safety over the road. However, there are a number of issues in exchanging traffic data over high mobility of VANET, such as broadcast storms, hidden nodes and network instability. This paper proposes a new model to detect the traffic conditions using clustering traffic situations that are gathered from the nodes (vehicles) in VANET. The model designs new principles of multi-level clustering to detect the traffic condition for road users. Our model (a) divides the situations of vehicles into clusters, (b) designs a set of metrics to get the correlations among vehicles and (c) detects the traffic condition in certain areas. These metrics are simulated using the network simulator environment (NS-3) to study the effectiveness of the model