Document Type : Original Article
Authors
1
Master's student, Shahid Bahonar University of Kerman, Kerman, Iran
2
Associate Professor, Shahid Bahonar University of Kerman, Kerman, Iran
3
Assistant Professor, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Vehicular ad hoc networks is one of the examples of intelligentization is the development of transportation infrastructure , which has led to the creation of new architectures based on cloud and fog computing. Mobilility and high speed of vehicles lead to instability in these types of networks and create obstacles in reliable information sharing. Clustering-based solutions are an optimal solution for network stability, achieving various features such as service quality and information dissemination. Among the issues that play an effective role in the stability of the cluster, we can mention how to build the cluster, how to choose the cluster head, and how to transfer data. In other words, one of most important challenges in these networks is how to cluster and select the cluster head. One of the main disadvantages of clustering algorithms is cluster instability and inappropriate cluster head selection. In this research, a two-level clustering method for case networks between vehicles is presented, whose lower level (the level close to the cars) is named as fog and its upper level (the level close to the infrastructure) is named as the cloud. Local data transmission and location management structures are performed at the fog level, and other operations related to data transfer to the infrastructure and sometimes distributed computing are performed through the cloud. The proposed method performs clustering with the criteria of speed, direction and location, and increases the stability of the cluster by selecting the appropriate cluster head. In addition, by providing a routing algorithm based on predicting the way cars move, the load is divided between the cluster head, the cloud, and the car. It can simultaneously improve the overhead and service quality of the inter-vehicle ad hoc network. Another disadvantage of the methods is the delay in sending information. The proposed method reduces the delay by making predictions and sending information correctly. The results of simulations show that compared to similar methods, the proposed method uses fewer steps to send information and improves the delay, delivery rate, and clustering overhead by 41, 18, and 29 percent respectively.
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