Communication in the Automotive Industry Inspired by Bio-Inspired Intelligent Clustering Algorithms and 5G-Enabled Architecture

One branch of the IoT that facilitates communication between vehicles is the Internet of Vehicles, more often known as the IoV. Because cars are always moving, the network's topology is also constantly changing. The appropriate routing distance, lifetime, and adaptability to changing networks are all significantly impacted by these IoV changes. Clustering is discussed in this article on the reliability of IoV architecture as a possible solution to these kinds of problems. The proposed metaheuristic clustering technique, the intelligent cluster algorithm based on the Internet of things (IoV-ICA), can be used to build a stable topology for cluster-based message route planning. In order to adapt the communication range to variations in road density, the research has been integrated into IoV-ICA. In order to assess how well the suggested IoV-ICA works, we compare it to state-of-the-art ACO benchmark methods. The goal of the transmission mode studies was to identify the topology with the highest degree of stability. When taking the channel's present status into account, clustering results using IoV-ICA are frequently the most condensed that can be obtained. Time to establish clusters, energy consumption, packet delivery ratio, throughput, and end-to-end delay are important elements in the clustering strategy. Compared to ACO-based classification, IoV-ICA performed better across all network topologies. Additional applications for 5G-enabled architecture include increasing network availability and integrating capabilities of cutting-edge communication systems.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/iccies63851.2025.11032945
Primary Topic
Bioinformatics and Genomic Networks
Type
article
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article

Communication in the Automotive Industry Inspired by Bio-Inspired Intelligent Clustering Algorithms and 5G-Enabled Architecture

Osama A. Mohammed, Majid A. Al-Taee, Rozi Mahmud, Amal Hasan Jumaa et al.
Bioinformatics and Genomic Networks
article

Communication in the Automotive Industry Inspired by Bio-Inspired Intelligent Clustering Algorithms and 5G-Enabled Architecture

Osama A. Mohammed, Majid A. Al-Taee, Rozi Mahmud, Amal Hasan Jumaa, Aaied Eqab Muraay Majed
article en

Abstract

One branch of the IoT that facilitates communication between vehicles is the Internet of Vehicles, more often known as the IoV. Because cars are always moving, the network's topology is also constantly changing. The appropriate routing distance, lifetime, and adaptability to changing networks are all significantly impacted by these IoV changes. Clustering is discussed in this article on the reliability of IoV architecture as a possible solution to these kinds of problems. The proposed metaheuristic clustering technique, the intelligent cluster algorithm based on the Internet of things (IoV-ICA), can be used to build a stable topology for cluster-based message route planning. In order to adapt the communication range to variations in road density, the research has been integrated into IoV-ICA. In order to assess how well the suggested IoV-ICA works, we compare it to state-of-the-art ACO benchmark methods. The goal of the transmission mode studies was to identify the topology with the highest degree of stability. When taking the channel's present status into account, clustering results using IoV-ICA are frequently the most condensed that can be obtained. Time to establish clusters, energy consumption, packet delivery ratio, throughput, and end-to-end delay are important elements in the clustering strategy. Compared to ACO-based classification, IoV-ICA performed better across all network topologies. Additional applications for 5G-enabled architecture include increasing network availability and integrating capabilities of cutting-edge communication systems.

University of Kirkuk (IQ), Al-Nisour University College (IQ)
Industry, innovation and infrastructure
Openalex Percentile: Top 87%
Bioinformatics and Genomic Networks
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