An Adaptable Mobility-Aware Clustering Algorithm in Vehicular Networks Mildred M. Caballeros Morales, Choong Seon Hong Networking Lab. Dept of Computer Engineering Kyung Hee University South Korea madai@networking.khu.ac.kr, cshong@khu.ac.kr Young-Cheol Bang Dept of Computer Engineering Korea Polytechnic University South Korea ybang@kpu.ac.kr Abstract—The forthcoming Intelligent Transportation System aims to achieve safety and productivity in transportation using vehicular ad hoc networks (VANETs) to support the communications system required. Currently, some clustering approaches have been proposed to improve the performance of VANETs due to their dynamic nature, high scalability and load balancing results. However, the host mobility and the constantly topology change continue to be main problems of this technique due to the lack of models which represent the vehicular behavior and the group mobility patterns. Therefore, we propose an Adaptable Mobility-Aware Clustering Algorithm based on Destination positions (AMACAD) to accurately follow the mobility pattern of the network prolonging the cluster lifetime and reducing the global overhead. In an effort to show the efficiency of AMACAD, a set of simulation was executed. The obtained results reveal an outstanding performance in terms of the lifetime of the cluster heads, lifetime of the members and the re-affiliation rate under varying speeds and transmission ranges. Keywords:Clustering Algorithm; Vehicular Networks; VANET; Intelligent Transportation System ITS; Mobility Aware; Destination-Based. I. INTRODUCTION The vehicular network VN has arisen as a profitable and auspicious network that improves the ubiquitous communication services across the moving vehicles. However, nowadays the critical issues in this area are the lack of reliable communication channels and the high delay of the communications, due to the heterogeneous nature and mobility of the environment. The relevance of these problems is shown practically in urban scenario in which using an Intelligent Transportation System (ITS), the vehicles share information with each other and with a local server through base stations (BSs) to avoid traffic congestion on the road. Moreover, a communication link exists between the local server and the traffic lights to control and divert traffic in case of an accident or traffic congestion occurs. In this scenario the low delivery rate, the temporary fragmentation and the congestion at the BS and local server are evident. To overcome these challenges we propose a mobility- aware clustering algorithm to decrease the delay of the communications, the bottlenecks, the overhead and the temporary fragmentation, considering the vehicular behavior and group mobility patterns. Some cluster-based approaches have been applied in VANETs, because the clusters reduce the overhead and delay, solving the scalability problem, providing an efficient resource consumption and load balance in large scale networks. However, in a high mobility environment the clusters usually are unstable and the clustering/de-clustering is constantly executed. The unstable issue is addressed in MobHiD [1], by proposing a predictive approach to arrange the clusters by selecting the node which is relatively stable as cluster head (CH), and using trees of neighborhood. It does not consider the fast mobility that occurs within VANETs. In the other hand the selection of the CH could be determined by the transmission speed and the quantity of mobile nodes [2]. Nevertheless, the authors do not consider the patterns and behavior of the vehicles. DGMA [3] is the approach that better considers the behavior of the vehicles, using the speed and direction parameters, but without regard of their destination. The destination of the vehicles is a key feature to model the group mobility and the behavior of the vehicles, enhancing the cluster stability and cluster lifetime. Therefore, our clustering algorithm takes into account the destination of vehicles, including the current location, speed, relative destination and final destination of vehicles as parameter to arrange the clusters. In this manner, the clustering algorithm resembles a natural model of location references, which helps to manage the mobility by improving the lifetime of the cluster and decreasing the number of cluster head changes and the number of cluster re-affiliations. The information is disseminated by groups enhancing the communication delay, reliability, low data delivery and congestion issues, making the vehicular networks accurate and efficient. The rest of the paper is structured as follows. Section II gives an overview of the approaches related to the clustering algorithms within a mobile environment. In section III we explain the scenario, the network model and the assumptions of the proposal. In section IV, the destination-based clustering algorithm is described. Section V shows the performance and the evaluation of the proposal. Finally, the conclusions of our work are presented in section VI. II. RELATED WORK Clustering algorithms have been proposed for vehicular networks. MobHiD [1] estimates the future mobility of nodes predicting the probability that the current neighborhood of a mobile node will remain the same through the received signal power to estimate the distance between two nodes. The drawback of the prediction method is the lack of accuracy in some cases. MOBIC [4], calculates the variance of relative mobility of a mobile node with each of its neighbors, where a small value of variance indicates the mobile node is moving This work was supported by the IT R&D program of MKE/KEIT [2001- S-014-01, On the development of Sensing based Emotive Service Mobile Handheld Devices]. Dr. CS Hong is the corresponding author.