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Reliable and Flexible Multi-Criteria Clustering Model for Internet of Vehicles

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Reliable and Flexible Multi-Criteria Clustering Model for Internet of Vehicles

Document Type : Original Article

Authors

1 Ph.D Student, Department of Information Technology Management, Science and Research branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Architecture and Computer Networks, Amirkabir University of Technology, Tehran, Iran.

3 Assistant Professor, Department of Industrial Management, Science and Research branch, Islamic Azad University, Tehran, Iran.

Abstract

The Internet of Vehicles is a new framework for intelligent transportation systems. One of its goals is to improve safety and increase the quality of road travels. Topology changes in IoV present significant challenges to the safety programs. Due to the variety of traffic conditions, the reliability of current clustering methods faces many risks. In this research, a multi-criteria clustering model called RFCV has been proposed with the aim of increasing the reliability of the Internet of Vehicles. This model is independent of infrastructure and introduces four new criteria: "history of vehicle movement," "conformity of vehicle speed with the harmonic average of nearby vehicles'''' speeds," "number of reliable neighbors," and "performance quality in previous clusters." The weight of moving vehicles is considered, and the one with the best weight is selected as the cluster head, while an alternative cluster head is also determined to improve cluster stability. The stability of the cluster ensures that message exchange is possible in the closest time to real-time. The efficiency of the proposed clustering model has been theoretically proven, and simulations with multiple scenarios in SUMO and NS3 environments demonstrate the superiority of RFCV in increasing "route lifetime and packet delivery rate (PDR)" and decreasing "average delay and control overhead" in both densely populated urban and less-populated highway environments.

Keywords

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Journal of Industrial Management Perspective
Volume 13, Issue 1, Spring 2023 - Serial Number 49
March 2023
Pages 41-77
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