Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow

Chen et al. EURASIP Journal on Wireless Communications and Networking Distributed multi-hop clustering algorithm for VANETs based on neighborhood follow Yuzhong Chen 0 1 Mingyue Fang 0 1 Song Shi 0 1 Wenzhong Guo 0 1 Xianghan Zheng 0 1 0 Fujian Key Laboratory of Network Computing and Intelligent Information Processing , No. 2 Xue Yuan Road, 350108 Fuzhou , China 1 College of Mathematics and Computer Science, Fuzhou University , No. 2 Xue Yuan Road, 350108 Fuzhou , China Vehicular ad hoc networks (VANETs) have become important components of metropolitan area networks, and clustering for VANETS provides many advantages. However, the stability of current clustering algorithms exhibits poor robustness because a VANET is a highly dynamic scenario. In this study, a novel multi-hop clustering scheme for VANETs, which generates cluster heads (CHs) via neighborhood follow relationship between vehicles, is proposed. The scheme is based on a reasonable assumption that a vehicle cannot certainly identify which vehicle in its multi-hop neighbors is the most suitable to be its CH, but it can easily grasp which vehicle in one-hop distance is the most stable and similar with it, and thus, they most likely belong to the same cluster. Consequently, a vehicle can choose its CH by following the most stable vehicle. The relative mobility between two vehicles combining the gains based on the followed number and the historical following information enables a vehicle to select which target to follow. Extensive simulation experiments are conducted to validate the performance of the proposed clustering scheme. VANETs; Clustering; Multi-hop; Neighborhood follow 1 Introduction As a new form of mobile ad hoc networks (MANETs), the vehicular ad hoc network (VANET) has emerged with the rapid development of radio technology that allows vehicle-to-vehicle communication [1]. VANET is one of the important components of an intelligent transport system because it holds great potential in traffic accident warning, traffic flow control, as well as in providing information services and extra serviceability. Clustering in VANET exhibits good scalability, because clustering can provide a simple information management mechanism and improve communication efficiency. Therefore, clustering algorithms for VANETs are attracting increasing attention. Unlike traditional MANETs, VANETs exhibit new features such as rapid movement of vehicles, frequent changing of network topology, and limited driving directions; moreover, it does not consider energy problems [2]. These particular features are the reason why traditional clustering algorithms for MANETs can hardly be applied in VANETs. Cluster stability is an important requirement in VANETs. Vehicles move fast which makes clusters broken easily and further affects the routing efficiency. Moreover, unstable clusters are prone to generate more control packets in VANETs and make the networks overload. Considerable research has explored clustering algorithms for VANETs to satisfy the requirements of their new features [3-10]. Most of these studies are based on one-hop clustering, which only allows communication between a cluster member (CM) and its cluster head (CH) with one-hop distance. The coverage of clusters is small in one-hop clustering, which leads to excess CHs and highmaintenance overhead. Consequently, several multi-hop clustering algorithms have been proposed in the past years [11-13]. These algorithms can extend the coverage of clusters, reduce the number of CHs, and improve cluster stability. However, some issues remain in multi-hop clustering for VANETs. For example, cluster stability must be further improved and maintenance cost must be reduced. Thus, comprehensive solutions must be developed. This study proposes a distributed multi-hop clustering algorithm for VANETs based on neighborhood follow (DMCNF) to enable fast and stable network setup. The main idea of this study is explained as follows. In largescale and complex VANETs, a vehicle can hardly acquire precise details of multi-hop distanced vehicles and can hardly decide which CH to choose among multi-hop neighbors. However, a vehicle can easily master local information and determine which vehicle within a onehop distance is the most stable/similar to it, and thus, they most likely belong to the same cluster. Consequently, a vehicle can choose its CH by following the most similar vehicle. This mechanism is identified as the neighborhood follow relationship. In turn, selecting CHs becomes a feedback from the neighborhood follow relationship. Accordingly, this study attempts to quickly obtain the aforementioned relationship chain from largescale VANETs. CHs are then chosen according to the obtained relationship among vehicles, and other vehicles directly or indirectly follow CHs to form clusters. The main contributions of this work are as follows. (1)A novel cluster model based on one-hop neighborhood follow is proposed. In the model, a cluster has a CH, which is directly or indirectly followed by other vehicles. The structure of DMCNF can steadily evolve in highly dynamic VANETs. (2)A neighborhood follow strategy is introduced for vehicles to choose and follow stable target vehicles from one-hop neighbors. Through this strategy, vehicles can adaptively update neighborhood follow information. Consequently, clusters are formed and maintained. (3)Through the neighborhood follow strategy, clusters are formed and maintained in a distributed manner. Vehicles are only required to regularly communicate with its one-hop neighbors for updating the neighborhood following information and maintaining clusters. (4)DMCNF does not depend on location service but still provides grouping of related vehicles and fast response to topology changes. The rest of this paper is organized as follows. Section 2 discusses the review of related literature, and Section 3 provides the preliminaries of the study. Section 4 describes the DMCNF algorithm, and Section 5 presents the experimental results. Finally, Section 6 concludes the paper and suggests a potential subject for future work. 2 Related work Clustering is a well-known means of organizing networks in MANETs. Many clustering solutions, including identifier neighbor-based clustering, topology-based clustering, mobility-based clustering, energy-based clustering, and weight-based clustering, have been proposed [14-21]. However, these clustering solutions significantly differ from vehicular clustering. MANETs are primarily limited because of their energy [22] and processing power; hence, their clustering is optimized for low-resource usage. However, vehicles are not only rich in resources, but they are also highly mobile. Consequently, clustering algorithms for MANETs are not effective in VANETs, and new solutions must be developed. Mobility-based clustering algorithm (MOBIC) [16] is a popular clustering algorithm mentioned in various studies. This approach is based (...truncated)