VANet security challenges and solutions: A survey

A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles—most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming—the lack of realistic vehicular channel modeling—is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

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A Comparative Survey of VANET Clustering Techniques

Vehicular Ad-hoc Networks (VANETs) are an emerging field, whereby vehicle-to-vehicle communications can enable many new applications such as safety and entertainment services. Most VANET applications are enabled by different routing protocols. The design of such routing protocols, however, is quite challenging due to the dynamic nature of nodes (vehicles) in VANETs. To exploit the unique characteristics of VANET nodes, we design a moving-zone based architecture in which vehicles collaborate with one another to form dynamic moving zones so as to facilitate information dissemination. We propose a novel approach that introduces moving object modeling and indexing techniques from the theory of large moving object databases into the design of VANET routing protocols. The results of extensive simulation studies carried out on real road maps demonstrate the superiority of our approach compared with both clustering and non-clustering based routing protocols.

MoZo: A Moving Zone Based Routing Protocol Using Pure V2V Communication in VANETs

Clustering in vehicular ad hoc networks

VWCA: An efficient clustering algorithm in vehicular ad hoc networks

The application of Mobile Ad Hoc Network (MANET) technologies in the service of Intelligent Transportation Systems (ITS) has brought new challenges in maintaining communication clusters of network members for long time durations. Stable clustering methods reduce the overhead of communication relay in MANETs and provide for a more efficient hierarchical network topology. During creation of VANET clusters, each vehicle chooses a head vehicle to follow. The average number of cluster head changes per vehicle measures cluster stability in these simulations during the simulation. In this paper we analyze the effect of weighting two well-known clustering methods with the vehicle-specific position and velocity clustering logic to improve cluster stability over the simulation time.

Cluster-Based framework in vehicular ad-hoc networks

Inter-vehicle communication by means of wireless Ad Hoc networking has the potential to improve traffic safety and comfort tremendously. Therefore, the application of Vehicular Ad Hoc Networks (VANETs) in the service of Intelligent Transportation Systems (ITS) has been highly focused in recent years. Derived from the successful outcome of a cluster-based framework in Mobile Ad Hoc Networks (MANETs), we apply this network topology to VANETs. Unfortunately, previous studies lack realistic modeling of vehicle mobility and evaluation of clustering performance so they may not correlate well with performance in a real deployment. Hence, in this paper, we propose a realistic micro- simulation model with the hope of contributing to clustering research in VANETs, and demonstrate how clustering algorithms work on it.

Traffic model for clustering algorithms in vehicular ad-hoc networks

The application of Mobile Ad Hoc Network (MANET) technologies to Vehicular Ad Hoc Networks (VANETs) in the service of Intelligent Transportation Systems (ITS) has brought new challenges in maintaining communication clusters of network members for long time durations. Stable clustering methods reduce the overhead of communication relay in MANETs and provide a more efficient hierarchical network topology. During the creation of clusters, one vehicle node per cluster is selected as the cluster head to act as the routing node. In this paper, we propose a new distributed algorithm which takes into consideration the moving direction of vehicles and leadership duration of cluster heads. A simulation study has been conducted in an innovative and realistic vehicular network model to determine which algorithm provides optimum stability over the simulation timeline. The results show that the proposed clustering logic significantly improves cluster stability under such circumstances.

A Novel Direction-Based Clustering Algorithm in Vehicular Ad Hoc Networks

The application of Mobile Ad Hoc Network (MANET) technologies in the service of Intelligent Transportation Systems (ITS) has brought new challenges in maintaining communication clusters of network members for long time durations. Stable clustering methods reduce the overhead of communication relay in MANETs and provide for a more efficient hierarchical network topology. During the creation of Vehicle Ad Hoc Network (VANET) clusters, one vehicle node per cluster is selected as the cluster head to act as the routing node. In this paper, we propose a Compound Utility Function (CUF) clustering algorithm which takes into consideration the degree, position, velocity and acceleration of a vehicle altogether, and the invocation of this algorithm is not periodic as in earlier research, but reactive on the dynamism of the nodes. In this paper experimentally compares CUF with Highest-Degree and Lowest-ID algorithm. The results show that CUF gives the best cluster stability.

An Improved Compound Clustering Algorithm in Vehicular Ad-Hoc Networks

This paper discusses image processing algorithms for the recognition of environmental and road conditions from real-time camera images. This research addresses various implementation techniques for and considerations of the implementation of algorithms to extrapolate various features from images taken by stationary traffic cameras. These algorithms have uses in the areas of traffic pattern analysis, emergency response, real-time traffic situation awareness, and homeland security. This paper covers the design and implementation of an automated camera heading detection system in order to determine the directional components of a camera’s position using the current camera image, various computer vision techniques, and a series of classification training images. Image processing topics addressed as part of this research include edge detection, line detection, and tow-dimensional filtering.

James G. Haran

Papers

Cluster-Based framework in vehicular ad-hoc networks

Traffic model for clustering algorithms in vehicular ad-hoc networks

A Novel Direction-Based Clustering Algorithm in Vehicular Ad Hoc Networks

An Improved Compound Clustering Algorithm in Vehicular Ad-Hoc Networks

Realtime Image Processing Algorithms for the Detection of Road and Environmental Conditions