Abstract
Over the last few years, Vehicular Ad Hoc Networks (VANETs) have emerged as a new class of efficient information dissemination technology among communities of users mainly because of their wide range of applications such as Intelligent Transport Systems (ITS), Safety applications, and entertainment during the mobility of the vehicles. Vehicles in VANETs act as an intelligent machine, which provides various resources to the end users with/without the aid of the existing infrastructure. But due to the high mobility and sparse distribution of the vehicles on the road, it is a challenging task to route the messages to their final destination in VANETs. To address this issue, clustering has been widely used in various existing proposals in literature. Clustering is a mechanism of grouping of vehicles based upon some predefined metrics such as density, velocity, and geographical locations of the vehicles. Motivated from these factors, in this paper, we have analyzed various challenges and existing solutions used for clustering in VANETs. Our contributions in this paper are summarized as follows: Firstly, a complete taxonomy on clustering in VANETs has been provided based upon various parameters. Based upon this categorization, a detailed discussion is provided for each category of clustering which includes challenges, existing solutions and future directions. Finally, a comprehensive analysis of all the existing proposals in literature is provided with respect to number of parameters such as topology selected, additional infrastructure requirements, road scenario, node mobility, data handled, and relative direction, density of the nodes, relative speed, communication mode, and communication overhead. The analysis provided for various existing proposals allows different users working in this domain to select one of the proposals with respect to its merits over the others.
•Clustering in VANETs with respect to various challenges and issues is explored.•Detailed taxonomy for clustering in VANETs is provided in the text.•Comparison of various clustering approaches with respect to various metrics.