面向QoS需求的分簇自组织网络路由算法

doi:10.13190/j.jbupt.2021-048

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (1): 1-6.doi: 10.13190/j.jbupt.2021-048

• 论文 • 下一篇

面向QoS需求的分簇自组织网络路由算法

杨灿¹, 罗涛¹, 刘颖¹, 李泽旭², 徐永庆²

1. 中国电子科技集团公司第七研究所, 广州 510000;
2. 北京邮电大学信息与通信工程学院, 北京 100876

收稿日期:2021-03-26 出版日期:2022-02-28 发布日期:2021-12-16
通讯作者: 李泽旭(1991—),男,博士生,邮箱:vinzxoh@bupt.edu.cn E-mail:vinzxoh@bupt.edu.cn
作者简介:杨灿(1976—),男,副总工程师
基金资助:
科技部国家重点研发计划项目（2017YFB0503000）

QoS Routing Algorithm in Clustered Self-Organizing Networks

YANG Can¹, LUO Tao¹, LIU Ying¹, LI Zexu², XU Yongqing²

1. China Electronic Technology Group Corporation Seventh Research Institute, Guangzhou 510000, China;
2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2021-03-26 Online:2022-02-28 Published:2021-12-16

摘要/Abstract

摘要： 基于分布式分簇的网络管理架构，网络节点可以被划分成多个管理域，并由相应区域的簇首进行协同管理。为实现分布式网络场景中，业务差异化的服务质量（QoS）需求与多维度网络资源之间的高效按需匹配，提出了一种基于强化学习的路由调度算法，以降低端到端的时延和防止网络拥塞为目标，优化调度路径。所提算法可以通过簇首集中式和节点分布式2种方式实现，可以解决分布式环境下全局资源信息不完备的问题，有效保证跳变环境下网络的健壮性。将100个节点划分为4个管理域进行仿真验证。仿真结果表明，所提算法可以有效地降低业务的平均时延，并且在业务拒绝率、网络资源利用率方面均优于传统方法。

关键词: 多跳网络, 服务质量路由, 强化学习

Abstract: Based on the distributed and clustering network architecture, network nodes can be divided into multiple clusters, which can be managed by their corresponding cluster heads in a collaborative manner. In order to achieve on-demand, efficient matches between the differentiated quality of service (QoS) requirements and the multi-dimensional network resources, a reinforcement learning-based routing algorithm is proposed. The proposed algorithm aims to reduce end-to-end delay and prevent congestion by optimizing the routing path, which can be implemented in both centralized cluster heads and distributed nodes, so as to guarantee the robustness in a dynamic environment. The performance is evaluated by numerical simulations in a network with 100 nodes divided into four regions. The simulation results illustrate that the proposed algorithm can reduce average latency significantly. Besides, the algorithm proposed is superior to the minimum-hop method in terms of rejection rate and resource utilization.

Key words: multi-hop network, quality of service routing, reinforcement learning

中图分类号:

TN929.52

杨灿, 罗涛, 刘颖, 李泽旭, 徐永庆. 面向QoS需求的分簇自组织网络路由算法[J]. 北京邮电大学学报, 2022, 45(1): 1-6.

YANG Can, LUO Tao, LIU Ying, LI Zexu, XU Yongqing. QoS Routing Algorithm in Clustered Self-Organizing Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(1): 1-6.

参考文献

[1] RUI L L, QIU X S, LI W J, et al. Autonomic management architecture and its application in mobile self-organi- zing network[J]. Journal of Beijing University of Posts and Telecommunications, 2011, 34(S1):63-67, 72. 芮兰兰, 邱雪松, 李文璟, 等. 面向移动自组织网络的自主管理架构及应用[J]. 北京邮电大学学报, 2011, 34(S1):63-67, 72.
[2] ZHAN G Z, CHEN L X, FANG W J, et al. Research on optimal routing of metro mesh system based on Dijkstra algorithm[J]. Telecom Engineering Technics and Stan-dardization, 2020, 33(6):77-81. 湛广志, 陈銮雄, 方伟津, 等. 基于Dijkstra算法的城域mesh系统最佳路由研究[J]. 电信工程技术与标准化, 2020, 33(6):77-81.
[3] CAO X F, ZHANG J. Research on WSN node routing optimization strategy based on improved genetic algorithm[J]. Yinshan Academic Journal, 2018, 32(4):66-70. 操晓峰, 张健. 一种基于改进遗传算法的WSN节点路由优化策略[J]. 阴山学刊, 2018, 32(4):66-70.
[4] JIANG Z, ZHANG H K, ZHANG L Y. The application of genetic algorithm in multicast routing of multimedia stream[J]. Journal of Beijing University of Posts and Telecommunications, 2004, 27(2):39-43. 姜圳, 张宏科, 张礼勇. 基于遗传算法的流媒体组播路由选择方法[J]. 北京邮电大学学报, 2004, 27(2):39-43.
[5] SUTTON R S, BARTO A G. Reinforcement learning:an introduction[M]. Cambridge:MIT Press, 1998:225-228.
[6] MAMMERI Z. Reinforcement learning based routing in networks:review and classification of approaches[J]. IEEE Access, 2019, 7:55916-55950.
[7] LI Y, WANG F, JING D S, et al. A Q-learning based routing approach for wireless sensor network[J]. Computing Technology and Automation, 2017, 36(2):155-160. 李荥, 王芳, 景栋盛, 等. 一种基于Q学习的无线传感网络路由方法[J]. 计算技术与自动化, 2017, 36(2):155-160.
[8] ZHOU Y, CAO T, XIANG W. Anypath routing protocol design via Q-learning for underwater sensor networks[J]. IEEE Internet of Things Journal, 2021, 8(10):8173-8190.
[9] ZHANG Y, ZHANG Z M, CHEN L, et al. Reinforcement learning-based opportunistic routing protocol for underwater acoustic sensor networks[J]. IEEE Trans on Veh Technol, 2021, 70(3):2750-2770.
[10] DHURANDHER S K, SINGH J, OBAIDAT M S, et al. Reinforcement learning-based routing protocol for opportunistic networks[C]//ICC 2020. Dublin:IEEE Press, 2020:1-6.
[11] DING R J, XU Y D, GAO F F, et al. Deep reinforcement learning for router selection in network with heavy traffic[J]. IEEE Access, 2019, 7:37109-37120.
[12] GUO X C, LIN H, LI Z Y, et al. Deep reinforcement learning based QoS-aware secure routing for SDN-IoT[J]. IEEE Internet of Things Journal, 2020, 7(7):6242-6251.

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面向QoS需求的分簇自组织网络路由算法

QoS Routing Algorithm in Clustered Self-Organizing Networks

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