A Clustering Routing Algorithm Based on Improved Ant Colony Optimization for an Urban Rail Transit Ad Hoc Network

As traffic pressure increases, urban rail transit has attracted attention because of its high speed and growing capacity in recent years. However, the traditional vehicle–ground communication systems of urban rail transit have been unable to meet the increasing requirements for communication. In thi...

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Veröffentlicht in:Applied sciences Jg. 13; H. 21; S. 11769
Hauptverfasser: Su, Zhaoyang, Xiao, Xiao, Han, Zijie, Liu, Liu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.11.2023
Schlagworte:
ad hoc network
Advertising executives
Algorithms
ant colony optimization
Clustering
clustering routing algorithm
Communications networks
Energy consumption
Infrastructure
Internet
Light rail transit
Machine learning
Maintenance costs
Optimization
Trains
urban rail transit
Vehicles
Wireless communications
China
ISSN:2076-3417, 2076-3417
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Zusammenfassung:As traffic pressure increases, urban rail transit has attracted attention because of its high speed and growing capacity in recent years. However, the traditional vehicle–ground communication systems of urban rail transit have been unable to meet the increasing requirements for communication. In this paper, we introduce an ad hoc network for urban rail transit to improve communication reliability, reduce end-to-end latency, and improve throughput of vehicle–ground communication. A novel clustering algorithm based on improved ant colony optimization is presented, which integrates various variables to elect cluster heads and adopts a low-latency queuing strategy. In addition, the algorithm can also replace the cluster heads adaptively to optimize the communication quality. Through the simulation results, the proposed algorithm can reduce the packet loss rate by about 60%, reduce the delay by about 50%, and improve the throughput by about 40% compared with the classical clustering routing protocol.
Bibliographie:ObjectType-Article-1
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ISSN:2076-3417
2076-3417
DOI:10.3390/app132111769