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Routing Optimization of LEO Satellite Network Based on Genetic Ant Colony Algorithm

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

Traditional satellite networks should not guarantee Quality of Service (QoS) due to unbalanced resource utilization and high load. Therefore, a routing algorithm based on load balancing is proposed, and a genetic ant colony algorithm is used to guarantee multi-constrained QoS. Firstly, the potential traffic demand of the whole network is predicted and the heuristic function is optimized. Then, the path cost and pheromone update strategy are improved. Finally, the optimal path satisfying load balancing and QoS constraints is selected. Through simulation experiments, it is found that the proposed algorithm can effectively balance the service load of the satellite network, and significantly improve the performance in the aspects of end-to-end delay and packet loss rate.

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Author information

Authors and Affiliations

  1. Beijing Research Institute of Telemetry, Beijing, 100094, China

    Jingyu Miao, Zhichao Ma, Bin Liu, Shaohua Hu & Limin Zhang

  2. Qiqihar University, Qiqihar, 161006, China

    Guolong An

Authors
  1. Jingyu Miao
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  2. Zhichao Ma
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  3. Bin Liu
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  4. Shaohua Hu
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  5. Limin Zhang
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  6. Guolong An
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Corresponding author

Correspondence to Limin Zhang .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Miao, J., Ma, Z., Liu, B., Hu, S., Zhang, L., An, G. (2024). Routing Optimization of LEO Satellite Network Based on Genetic Ant Colony Algorithm. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_37

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_37

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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