A New Hierarchical Multi-granularity Cross-domain Addressing Approach in Datalink Networks

被引:0
|
作者
Li, Chunfeng [1 ]
Wang, Zhenlei [1 ]
Wu, Xiongjun [2 ]
机构
[1] China Elect Technol Grp Corp CETC54, Gen Dept Networks & Commun, Res Inst 54, Shijiazhuang 050081, Hebei, Peoples R China
[2] Eighth Acad China Aerosp Sci & Technol Corp, Shanghai Acad Space Flight Technol, Natl Key Lab Scattering & Radiat, Inst 802, Shanghai 201109, Peoples R China
来源
2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 | 2024年
基金
中国国家自然科学基金;
关键词
Multi-granularity; addressing mode; addressing system; Data link; Link state routing protocol;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we suggest a new hierarchical multi-granularity cross-domain addressing approach in datalink networks. The design of a multi-granularity cross-domain addressing mode for data link networks aims to overcome the limitations in capacity and flexibility of traditional fixed-length addressing modes.The traditional data link mainly completes the addressing between the members of a single data link, and the message cannot be exchanged between different data links, and the fixed length of addressing limits the number of data link members. This new addressing mode consists of three segments: network segment address, node segment address, and payload segment address. It effectively meets the requirements for adaptive and efficient addressing. Additionally, this addressing design takes into consideration the need for fast addressing, network capacity, and compatibility with traditional data links. An addressing protocol is specifically designed to support this new mode. To accommodate the unique characteristics of data link networks and this multi-granularity cross- domain addressing mode, segmented addressing is proposed. The improved OLSR routing protocol is utilized to handle the network segment and node segment addresses by enhancing both HELLO messages and TC messages as well as their processing methods. This ensures that it can meet the demands of a multi-granularity cross-domain addressing mode for data link networks. The MPR nodes are calculated using a greedy algorithm while the entire network routing is determined through Dijkstra's algorithm. On the other hand, internally within each node, the payload segment address assignment is completed along with distributing received data to respective payloads. The effectiveness of this proposed addressing mode has been verified using Exata software which yielded the average delay was 0.0583s and the throughput was 3842.63bits/ second. Comparing the throughput of US military LINK4A at 5000bits/second and LINKl 1 at 2250bits/second, this design achieves better performance and can better meet the needs of data link applications. These results demonstrate its capability to fulfill various requirements in data link applications.
引用
收藏
页码:430 / 435
页数:6
相关论文
共 50 条
  • [1] Two novel multi-granularity optical cross-connect architectures for hierarchical optical networks
    Qi, Yongmin
    Tian, Xiangqing
    Jin, Yaohui
    Hu, Weisheng
    OPTICAL TRANSMISSION, SWITCHING, AND SUBSYSTEMS IV, PTS 1 AND 2, 2006, 6353
  • [2] MIMNet: Multi-interest Meta Network with Multi-granularity Target-guided Attention for cross-domain recommendation
    Zhu, Xiaofei
    Yin, Yabo
    Wang, Li
    NEUROCOMPUTING, 2025, 620
  • [3] Multi-domain grooming algorithm based on hierarchical integrated multi-granularity auxiliary graph in optical mesh networks
    Jingjing Wu
    Lei Guo
    Weigang Hou
    Photonic Network Communications, 2012, 23 : 205 - 216
  • [4] Multi-domain grooming algorithm based on hierarchical integrated multi-granularity auxiliary graph in optical mesh networks
    Wu, Jingjing
    Guo, Lei
    Hou, Weigang
    PHOTONIC NETWORK COMMUNICATIONS, 2012, 23 (03) : 205 - 216
  • [5] Hierarchical classification with exponential weighting of multi-granularity paths
    Wang, Yibin
    Zhu, Qing
    Cheng, Yusheng
    INFORMATION SCIENCES, 2024, 675
  • [6] Multi-granularity Characteristics Analysis of Software Networks
    Sun, Weiqiang
    Jin, Chunlin
    Liu, Ji
    EMERGING RESEARCH IN ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, 2012, 315 : 88 - +
  • [7] Clustering web documents using hierarchical representation with multi-granularity
    Huang, Faliang
    Zhang, Shichao
    He, Minghua
    Wu, Xindong
    WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, 2014, 17 (01): : 105 - 126
  • [8] Few-shot learning based on hierarchical classification via multi-granularity relation networks
    Su, Yuling
    Zhao, Hong
    Lin, Yaojin
    INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 2022, 142 : 417 - 429
  • [9] Clustering web documents using hierarchical representation with multi-granularity
    Faliang Huang
    Shichao Zhang
    Minghua He
    Xindong Wu
    World Wide Web, 2014, 17 : 105 - 126
  • [10] Service organization and recommendation using multi-granularity approach
    Liu, Jianxiao
    He, Keqing
    Wang, Jian
    Liu, Feng
    Li, Xiaoxia
    KNOWLEDGE-BASED SYSTEMS, 2015, 73 : 181 - 198