QoS Routing Algorithm in Clustered Self-Organizing Networks

doi:10.13190/j.jbupt.2021-048

Journal of Beijing University of Posts and Telecommunications ›› 2022, Vol. 45 ›› Issue (1): 1-6.doi: 10.13190/j.jbupt.2021-048

• PAPERS • Next Articles

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

Abstract

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

CLC Number:

TN929.52

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.

References

[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.

[1]	LIU Yang, TENG Yinglei, NIU Tao, ZHI Jialin. Filter Pruning Algorithm Based on Deep Reinforcement Learning [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 31-36.
[2]	TAN Weiqian, WU Binwei, WANG Shuo. Cross-Domain Deterministic Networking Architecture and DRL Flow Scheduling [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 37-42.
[3]	YANG Hua, GENG Xuan, KONG Ning. A MAC Protocol for Wireless Networks Using Dueling-DDQN Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 25-30.
[4]	. Deterministic Scheduling and Routing Joint Intelligent Optimization Scheme in Computing First Network [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(2): 9-14.
[5]	. An Ensemble Learning-Aided DDPG Resource Optimization Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(2): 29-36.
[6]	. Resource Allocation Based on Alternating Direction Multiplier Method and Deep Reinforcement Learning Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(6): 126-130.
[7]	. Intent-driven Demand-aware Resource Service in Autonomous Networks [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(6): 85-91.
[8]	CHEN Chunlei, LIU Kaijun, DONG Chen, ZHOU Hongyuan. An Adaptive Bit Rate Algorithm Model Based on Wireless Network [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(5): 115-120.
[9]	ZHI Jialin, WANG Nan, MAN Yi, TENG Yinglei. Hardware Model-Aware Joint Offloading and Resources Allocation Optimization [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 22-28.
[10]	DU Mei, ZHOU Junhua, LI Dunqiao, CHEN Shizhao, WEI Yifei. A Joint Intelligent Optimization Scheme of Computation Offloading and Resource Allocation for MEC [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(2): 65-71.
[11]	HUANG Hao, HU Zhi-qun, WANG Lu-han, LU Zhao-ming, WEN Xiang-ming. Intelligent Traffic Signal Control Algorithm Based on Sumtree DDPG [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 97-103.
[12]	GUAN Wan-qing, ZHANG Hai-jun, LU Zhao-ming. Intelligent Resource Allocation Algorithm for 6G Multi-Tenant Network Slicing Based on Deep Reinforcement Learning [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 132-139.
[13]	MA Qing-liu, YU Peng, WU Jia-hui, XIONG Ao, YAN Yong. A Integrated Energy Service Channel Optimization Mechanism Based on Deep Reinforcement Learning [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(2): 87-93.
[14]	LIU Jin-hua, KE Zhong-ming, ZHOU Wen-hui. Reinforcement Learning Based Energy Dispatch Strategy and Control Optimization of Microgrid [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 28-34.
[15]	CHENG Chao, TENG Jun-jie, ZHAO Yan-ling, SONG Mei. Traffic Distribution Algorithm Based on Multi-Agent Reinforcement Learning [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 43-48,57.

QoS Routing Algorithm in Clustered Self-Organizing Networks

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments