Congestion Control Method for Campus Opportunity Network based on Ant Colony Algorithm

Li Peng; Cao Yumei; Jia Huan; Wang Xiaoming; Wu Xiaojun

doi:10.23919/cje.2024.00.019

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Peng Li, Yumei Cao, Huan Jia, et al., “Congestion Control Method for Campus Opportunity Network based on Ant Colony Algorithm,” Chinese Journal of Electronics, vol. 34, no. 2, pp. 1–10, 2025 doi: 10.23919/cje.2024.00.019

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Peng Li, Yumei Cao, Huan Jia, et al., “Congestion Control Method for Campus Opportunity Network based on Ant Colony Algorithm,” Chinese Journal of Electronics, vol. 34, no. 2, pp. 1–10, 2025 doi: 10.23919/cje.2024.00.019

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Congestion Control Method for Campus Opportunity Network based on Ant Colony Algorithm

doi: 10.23919/cje.2024.00.019

Li Peng^{1, 2, 3, 4, 5
,},
Cao Yumei^{1, 2, 3, 4
,},
Jia Huan^{1, 2, 3, 4
,},
Wang Xiaoming^{1, 2, 3, 4, 5
,},
Wu Xiaojun^{1, 2, 3, 4, 5
,
,}

1.
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
2.
Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism, Xi’an 710119, China
3.
Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China
4.
Engineering Laboratory of Teaching Information Technology of Shaanxi Province, Xi’an 710119, China
5.
Xi’an Key Laboratory of Cultural Tourism Resources Development and Utilization, Xi’an 710062, China

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Author Bio:
Peng Li received his Ph.D. degree in educational technology from Beijing Normal University in 2010. Since July 2010, he has been engaged in teaching and research in the School of Computer Science, Shaanxi Normal University, Xi'an, China. He is an Associate Professor, Vice Dean, Member of Chinese Computer Society, Executive Director of Shaanxi Computer Education Society, member of Shaanxi Computer Society, and member of IEEE CS. His main research interests are internet of things, media computing, opportunistic networks, and educational information science and technology. (Email: lipeng@snnu.edu.cn)

Yumei Cao pursuing the M.S. degree with the School of Computer Science, Shaanxi Normal university, Xi’an, China. Her main research interests are opportunistic networks and mobile computing. (Email: cym41712121@snnu.edu.cn)

Huan Jia pursuing the M.S. degree with the School of Computer Science, Shaanxi Normal university, Xi’an, China. His main research interests are opportunistic networks, social network and mobile computing. (Email: jiahuan@snnu.edu.cn)

Xiaoming Wang received the Ph.D. degree in computer software and theory from Northwest University, Xi’an, China, in 2005. He is currently a Professor with the School of Computer Science, Shannxi Normal University, Xi’an, China. From 2007 to 2008, he was a Visiting Scholar with the Department of Computer Science, Georgia State University, Atlanta, GA, USA. His main research interests include internet of things, social computing, and artificial intelligence. (Email: wangxm@snnu.edu.cn)

Xiaojun Wu received his Ph.D. degree from Northwestern Polytechnical University, Xi'an, China. in 2005, and has been engaged in technology, teaching and research in Inventec Group, Huawei, and Northwestern Polytechnical University, Xi'an, China. He is a Professor and Ph.D. Supervisor at Shaanxi Normal University, Xi'an, China. He is the Project Leader of the National Key Research and Development Program, and the Director of the MindSpore Research Laboratory of Shaanxi Normal University and Huawei. His main research areas are machine learning, mobile self-organizing networks, intelligent interaction, and cultural and technological integration. (Email: xjwu@snnu.edu.cn)
Corresponding author: Email: xjwu@snnu.edu.cn
Received Date: 2024-01-26
Accepted Date: 2024-05-27

Available Online: 2024-06-14

Abstract

Abstract

Due to the limited storage resources of portable devices, congestion control has become a hot direction in opportunity networks. To address the issue of heavy loads on certain nodes, which can impact routing efficiency and overall network performance, this paper proposes a load-balancing algorithm based on ant colony optimization (ACO) in a campus environment. The congestion status is represented by the ratio of message drop receptions within a certain period and the occupancy of the cache. Path selection is based on the concentration of pheromones and the pheromones on the path are updated when a data transmission is completed. In the event of congestion, the algorithm prevents a large amount of data from entering the node and unloads the data to other nodes, even if they are not the optimal relay nodes. Experimental results demonstrate that the proposed algorithm effectively improves data transmission success rates, reduces network loads, and decreases the number of packet losses, especially under low latency conditions.
- Opportunity network,
- Routing protocol,
- Congestion control,
- Cache management

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References(36)

References

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