A New Hybrid Approach Cluster-Heads Election to reduce the number of clusters in VANETs
Vehicular Ad-hoc Networks (VANETs) is a subpart from mobile Ad-hoc networks (MANETs). The main idea from VANETs is to establish communication between vehicles for the safety of the people. However, mobility of vehicles appears to be the main challenge in VANETs. High speed of vehicles causes topolog...
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Published in | International Conference on Engineering Technology and their Applications (Online) pp. 436 - 440 |
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Main Authors | , , , , , , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
31.05.2022
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Subjects | |
Online Access | Get full text |
ISSN | 2831-753X |
DOI | 10.1109/IICETA54559.2022.9888442 |
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Summary: | Vehicular Ad-hoc Networks (VANETs) is a subpart from mobile Ad-hoc networks (MANETs). The main idea from VANETs is to establish communication between vehicles for the safety of the people. However, mobility of vehicles appears to be the main challenge in VANETs. High speed of vehicles causes topology changed that results in an unstable network. Therefore, clustering is used to reduce the effect of vehicle mobility by combining vehicles in a group called cluster. Network stability is based on clusters stability. Therefore, most of the authors focused on increasing cluster stability to enhance the network. Nevertheless, increasing the number of the cluster in the network affects the network performance negatively. This paper proposes a new method called Hybrid Cluster-Head Election (HCHE) to reduce the number of the cluster in the network to increase network performance. Performance of the proposed method was evaluated through simulation by utilising real-time datasets. Arduino, GPS and external antennas were used to collect real GPS locations from Batu Pahat City - Johor - Malaysia. Overall, the proposed method resulted high performance in the reduction ratio of the number of cluster head compared to other recent methods. The proposed method successfully reduced the number of cluster heads by 30%. |
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ISSN: | 2831-753X |
DOI: | 10.1109/IICETA54559.2022.9888442 |