A novel k-means powered algorithm for an efficient clustering in vehicular ad-hoc networks

Considerable attention has recently been given to the routing issue in vehicular ad-hoc networks (VANET). Indeed, the repetitive communication failures and high velocity of vehicles reduce the efficacy of routing protocols in VANET. The clustering technique is considered an important solution to overcome these difficulties. In this paper, an efficient clustering approach using an adapted k-means algorithm for VANET has been introduced to enhance network stability in a highway environment. Our approach relies on a clustering scheme that accounts for the network characteristics and the number of connected vehicles. The simulation indicates that the proposed approach is more efficient than similar schemes. The results obtained appear an overall increase in constancy, proven by an increase in cluster head lifetime by 66%, and an improvement in robustness clear in the overall reduction of the end-to-end delay by 46% as well as an increase in throughput by 74%

A Novel Stable Clustering Approach Based On Gaussian Distribution And Relative Velocity In VANETs

Vehicles in Vehicular Ad-hoc Networks (VANETs) are characterized by their high dynamic mobility (velocity). Changing in VANET topology is happened frequently which caused continuous network communication failures. Clustering is one of the solutions applied to reduce the VANET topology changes. Stable clusters are required and Indispensable to control, improve and analyze VANET. In this paper, we introduce a new analytical VANET's clustering approach. This approach aims to enhance the network stability. The new proposed grouping process in this study depends on the vehicles velocities mean and standard deviation. The principle of the normal (Gaussian) distribution is utilized and emerged with the relative velocity to propose two clustering levels. The staying duration of vehicles in a cluster is also calculated and used as an indication. The first level represents a very high stabile cluster. To form this cluster, only the vehicles having velocities within the range of mean ± standard deviation, collected in one cluster (i.e. only 68% of the vehicles allowed to compose this cluster). The cluster head is selected from the vehicles having velocities close to the average cluster velocity. The second level is to create a stable cluster by grouping about 95% of the vehicles. Only the vehicles having velocities within the range of mean ± 2 standard deviation are collected in one cluster. This type of clustering is less stable than the first one. The analytical analysis shows that the stability and the staying duration of vehicles in the first clustering approach are better than their values in the second clustering approach

Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks

Vehicular Ad hoc Network (VANET) clustering is an active research area where a group of connected vehicles forms an ad hoc network. A stable cluster is essential for routing and data dissemination in VANET to avoid various issues such as packet loss, broadcast storm, and increased overhead resulting in an unstable clustering. Therefore, clustering is regarded as an essential part of the hierarchy of intelligent transportation systems. The literature contains numerous approaches for VANETs clustering. The majority of the approaches follow heuristic-based protocol combined with various connected phases and processes, such as cluster formation, cluster head selection, and cluster maintenance. Due to the high mobility of vehicles in VANET, it is more attractive to adapt the evolving data clustering to an evolving VANET clustering framework. The inclusion of extended mobility features has not been observed in most of the clustering approaches. The required extended mobility features are essential to overcome the challenges of vehicle movement. Moreover, relying on the non-valid assumptions such as the nature of the spherical cluster and the pre-knowledge about the number of clusters may not be feasible in many cases. In addition, most of VANETs clustering approaches use simple evaluation methodology where most of the approaches disregard a significant issue in the evaluation methodology. This thesis presents VANETs clustering framework called Centre-based Evolving Clustering with Grid Partitioning (CEC-GP). This framework uses an evolving data clustering algorithm by adopting the concept of grid granularity to capture the features of a cluster more efficiently. CEC-GP includes extended mobility features and provides the capability to avoid spherical assumptions for clusters, which is employed in most of the distance-based clustering. Besides, this framework offers high performance even with the challenging and high mobility scenarios related to the variability of mobility behaviour. The developed CEC-GP also includes an integrated approach that combined all clustering tasks such as cluster formation, cluster head selection, and cluster maintenance. Finally, CEC-GP shows a better stability performance compared with "Centre-based Stable Clustering (CBSC)" and "Evolving Data Clustering Algorithm (EDCA)" based on different performance metrics such as the clustering efficiency, the cluster head, and cluster member duration, the cluster head change rate, and the number of created clusters. The performance evaluation results show CEC-GP is better compared with the other two benchmarks in term of stability and consistency

Proceedings of the 3rd International Conference on Models and Technologies for Intelligent Transportation Systems 2013

Challenges arising from an increasing traffic demand, limited resource availability and growing quality expectations of the customers can only be met successfully, if each transport mode is regarded as an intelligent transportation system itself, but also as part of one intelligent transportation system with “intelligent” intramodal and intermodal interfaces. This topic is well reflected in the Third International Conference on “Models and Technologies for Intelligent Transportation Systems” which took place in Dresden 2013 (previous editions: Rome 2009, Leuven 2011). With its variety of traffic management problems that can be solved using similar methods and technologies, but with application specific models, objective functions and constraints the conference stands for an intensive exchange between theory and practice and the presentation of case studies for all transport modes and gives a discussion forum for control engineers, computer scientists, mathematicians and other researchers and practitioners. The present book comprises fifty short papers accepted for presentation at the Third Edition of the conference. All submissions have undergone intensive reviews by the organisers of the special sessions, the members of the scientific and technical advisory committees and further external experts in the field. Like the conference itself the proceedings are structured in twelve streams: the more model-oriented streams of Road-Bound Public Transport Management, Modelling and Control of Urban Traffic Flow, Railway Traffic Management in four different sessions, Air Traffic Management, Water Traffic and Traffic and Transit Assignment, as well as the technology-oriented streams of Floating Car Data, Localisation Technologies for Intelligent Transportation Systems and Image Processing in Transportation. With this broad range of topics this book will be of interest to a number of groups: ITS experts in research and industry, students of transport and control engineering, operations research and computer science. The case studies will also be of interest for transport operators and members of traffic administration