Optimal Network Structuring for Large-Scale WSN with Virtual Broker based Publish/Subscribe

被引:0
|
作者
Liu, Yang [1 ]
Seet, Boon-Chong [1 ]
机构
[1] Auckland Univ Technol, Dept Elect & Elect Engn, Auckland, New Zealand
来源
PROCEEDINGS OF THE 2017 2ND WORKSHOP ON RECENT TRENDS IN TELECOMMUNICATIONS RESEARCH (RTTR) | 2017年
关键词
network structuring; wireless sensor network; virtual broker; publish/subscribe; analytical model; SENSOR; COMMUNICATION; ALGORITHM;
D O I
暂无
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Large-scale wireless sensor networks (WSNs) often need to be partitioned for efficient data acquisition. To enhance the scalability and reliability of publish/subscribe (pub/sub) systems implemented on a large-scale in WSNs, dedicated broker devices with large storage and computation capacities can be replaced with virtual brokers (VBs), each formed by multiple co-located sensor nodes that shared resources to collaboratively perform the role of a conventional broker. As communication is a major consumer of energy, which is a scarce resource in WSNs, how the WSN is structured in terms of the number of partitions and the size of VB can impact the energy consumption of the network. In this paper, we propose a novel approach to optimal network structuring of large-scale WSN with virtual brokers for pub/sub communication. Underlying the approach is an analytical model which determines the optimal number of partitions and VB size that jointly minimize the total communication overhead of the WSN.
引用
收藏
页码:64 / 68
页数:5
相关论文
共 50 条
  • [41] Credible seed identification for large-scale structural network alignment
    Wang, Chenxu
    Wang, Yang
    Zhao, Zhiyuan
    Qin, Dong
    Luo, Xiapu
    Qin, Tao
    DATA MINING AND KNOWLEDGE DISCOVERY, 2020, 34 (06) : 1744 - 1776
  • [42] Large-scale quantum networks based on graphs
    Epping, Michael
    Kampermann, Hermann
    Bruss, Dagmar
    NEW JOURNAL OF PHYSICS, 2016, 18
  • [43] Efficient and scalable reinforcement learning for large-scale network control
    Ma, Chengdong
    Li, Aming
    Du, Yali
    Dong, Hao
    Yang, Yaodong
    NATURE MACHINE INTELLIGENCE, 2024, 6 (09) : 1006 - 1020
  • [44] Analytical network modeling of heterogeneous large-scale cluster systems
    Javadi, Bahman
    Abawajy, Jemal H.
    Akbari, Mohammad K.
    Nahavandi, Saeid
    2006 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING, VOLS 1 AND 2, 2006, : 602 - +
  • [45] Dynamic large-scale network synchronization from perception to action
    Hirvonen, Jonni
    Monto, Simo
    Wang, Sheng H.
    Palva, J. Matias
    Palva, Satu
    NETWORK NEUROSCIENCE, 2018, 2 (04): : 442 - 463
  • [46] Neural-Fuzzy based effective clustering for large-scale wireless sensor networks with mobile sink
    Verma, Akshay
    Kumar, Sunil
    Gautam, Prateek Raj
    Kumar, Arvind
    PEER-TO-PEER NETWORKING AND APPLICATIONS, 2021, 14 (06) : 3518 - 3539
  • [47] Dynamic Relationship Network Analysis Based on Louvain Algorithm for Large-Scale Group Decision Making
    Li, Minxuan
    Qin, Jindong
    Jiang, Tao
    Pedrycz, Witold
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2021, 14 (01) : 1242 - 1255
  • [48] ECAR: Energy efficient cluster based adaptive routing for large scale WSN
    Sangeetha, K.
    Shanthini, J.
    Karthik, S.
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (05) : 7811 - 7825
  • [49] Large-Scale Emulation Network Topology Partition Based on Community Detection With the Weight of Vertex Similarity
    Yan, Jianen
    Xu, Haiyan
    Li, Ning
    Zhang, Zhaoxin
    COMPUTER JOURNAL, 2023, 66 (08) : 1817 - 1828
  • [50] A Practical Large-Scale Distribution Network Planning Model Based on Elite Ant-Q
    Wang, Ziyao
    Lin, Dan
    Zeng, Guangxuan
    Yu, Tao
    IEEE ACCESS, 2020, 8 : 58912 - 58922