Dynamic Multi Level Spatially Aware Clustering with Weighted Cluster Head Selection

Articles in Press

Document Type : Original Article

Authors

1 Department of Electricity and Computer, Faculty of Engineering, Kharazmi University, Tehran, Iran

2 Department of Data Science and Technology, Faculty of Intelligent Systems, University of Tehran, Tehran, Iran

Abstract

Vehicular Ad-hoc Networks are a growing technology that ensures safe and efficient road traffic management. Mobility management is the primary challenge in inter-vehicle networks. Therefore, an adaptive and stable clustering protocol is a crucial issue in inter-vehicle network technology. Day by day, inter-vehicle networks based on clustering are attracting more attention. The main variables used in forming clustering protocols are speed, distance, and direction of movement. However, in different methods, other variables such as confidence level, lifespan, and similarity measure, quality of communication link, signal noise rate, and packet transmission rate have been used alongside the three main components. In this method, we aim to improve aspects such as the number of clusters formed and the number of changes in cluster heads by employing various methods and techniques presented. The purpose of these improvements is to create more cohesion and stability in the formed clusters. In the proposed algorithm, Dynamic Multi Level Spatially Aware Clustering with Weighted Cluster Head Selection (DML-SAC-WCHS), we aim to improve the number of clusters formed by at least 5.7% by using spatial indexing and examining the linear position that the vehicle passes through. Additionally, we have achieved a 3.2% improvement in the number of cluster head changes.

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 31 October 2024

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History

  • Receive Date: 27 April 2024
  • Revise Date: 17 September 2024
  • Accept Date: 22 October 2024

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