高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于Stackelberg博弈的虚拟化无线传感网络资源分配策略

王汝言 李宏娟 吴大鹏

王汝言, 李宏娟, 吴大鹏. 基于Stackelberg博弈的虚拟化无线传感网络资源分配策略[J]. 电子与信息学报, 2019, 41(2): 377-384. doi: 10.11999/JEIT180277
引用本文: 王汝言, 李宏娟, 吴大鹏. 基于Stackelberg博弈的虚拟化无线传感网络资源分配策略[J]. 电子与信息学报, 2019, 41(2): 377-384. doi: 10.11999/JEIT180277
Ruyan WANG, Hongjuan LI, Dapeng WU. Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network[J]. Journal of Electronics & Information Technology, 2019, 41(2): 377-384. doi: 10.11999/JEIT180277
Citation: Ruyan WANG, Hongjuan LI, Dapeng WU. Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network[J]. Journal of Electronics & Information Technology, 2019, 41(2): 377-384. doi: 10.11999/JEIT180277

基于Stackelberg博弈的虚拟化无线传感网络资源分配策略

doi: 10.11999/JEIT180277
基金项目: 国家自然科学基金(61771082),重庆市高校创新团队建设计划资助项目(CXTDX201601020)
详细信息
    作者简介:

    王汝言:男,1969年生,教授,博士,研究方向为泛在网络、多媒体信息处理等

    李宏娟:女,1993年生,硕士生,研究方向为虚拟化、无线传感网络

    吴大鹏:男,1979年生,教授,博士,研究方向为泛在无线网络、无线网络服务质量控制等

    通讯作者:

    李宏娟 ilihj@foxmail.com

  • 中图分类号: TP393

Stackelberg Game-based Resource Allocation Strategy in Virtualized Wireless Sensor Network

Funds: The National Natural Science Foundation of China (61771082), The Chongqing Funded Project of Chongqing University Innovation Team Construction Plan (CXTDX201601020)
  • 摘要:

    虚拟化技术可有效缓解当前无线传感网络(WSN)中资源利用率较低、服务不灵活的问题。针对虚拟化WSN中的资源竞争问题,该文提出一种基于Stackelberg博弈的多任务资源分配策略。依据所承载业务的不同服务质量(QoS)需求,量化多个虚拟传感网络请求(VSNRs)的重要程度,进而,利用分布式迭代方法,获取WSN的最优价格策略和VSNRs的最优资源需求量,最后,根据纳什均衡所确定的最优价格、最优资源分配量,对多个VSNRs分配资源。仿真结果表明,所提策略不仅能满足用户的多样化需求,而且提升了节点和链路资源利用率。

  • 图  1  虚拟化无线传感网络示意图

    图  2  虚拟化前后节点缓存资源利用率

    图  3  虚拟化前后链路带宽资源利用率

    图  4  不同a值时VSNSP效用函数在迭代过程中的变化

    图  5  WSNInP与VSNSPs间的纳什均衡

    图  6  不同任务数产生的收益

    图  7  不同任务数的带宽利用率

    表  1  仿真参数设置

    参数设定参考数值
    仿真区域(m2)50×50
    节点数量(个)55
    节点处理速度(bit/s)16~32
    节点存储能力(kb)4~15
    节点能量(J)2~4
    链路带宽(kb/s)5~30
    用户体验常量1或2
    VSNR资源需求策略调节步长0.1
    WSN价格策略调节步长0.1
    最大迭代次数/次200
    下载: 导出CSV
  • EZDIANI S, ACHARYYA I S, SIVAKUMAR S, et al. Wireless sensor network softwarization: Towards WSN adaptive QoS[J]. IEEE Internet of Things Journal, 2017, 4(5): 1517–1527. doi: 10.1109/JIOT.2017.2740423
    LIAO Yizheng, MOLLINEAUX M, HSU R, et al. SnowFort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring[J]. IEEE Sensors Journal, 2014, 14(12): 4253–4263. doi: 10.1109/JSEN.2014.2358253
    HU Xiaoya, YANG Liuqing, and XIONG Wei. A novel wireless sensor network frame for urban transportation[J]. IEEE Internet of Things Journal, 2015, 2(6): 586–595. doi: 10.1109/JIOT.2015.2475639
    ALAIAD A and ZHOU Lina. Patients’ adoption of WSN-Based smart home healthcare systems: an integrated model of facilitators and barriers[J]. IEEE Transactions on Professional Communication, 2017, 60(1): 4–23. doi: 10.1109/TPC.2016.2632822
    PARK P, MARCO P D, and JOHANSSON K H. Cross-layer optimization for industrial control applications using wireless sensor and actuator mesh networks[J]. IEEE Transactions on Industrial Electronics, 2017, 64(4): 3250–3259. doi: 10.1109/TIE.2016.2631530
    KHAN I, BELQASMI F, GLITHO R, et al. Wireless sensor network virtualization: Early architecture research perspectives[J]. IEEE Network, 2015, 29(3): 104–112. doi: 10.1109/MNET.2015.7113233
    KHAN I, BELQASMI F, GLITHO R, et al. Wireless sensor network virtualization: A survey[J]. IEEE Communications Surveys & Tutorials, 2016, 18(1): 553–576. doi: 10.1109/COMST.2015.2412971
    GUO Lei, NING Zhaolong, SONG Qingyang, et al. A QoS-oriented high-efficiency resource allocation scheme in wireless multimedia sensor networks[J]. IEEE Sensors Journal, 2017, 17(5): 1538–1548. doi: 10.1109/JSEN.2016.2645709
    DELGADO C, BOUSNINA S, CESANA M, et al. On optimal resource allocation in virtual sensor networks[J]. Ad Hoc Networks, 2016, 50(C): 23–40. doi: 10.1016/j.adhoc.2016.04.004
    DELGADO C, CANALES M, ORTIN J, et al. Joint application admission control and network slicing in virtual sensor networks[J]. IEEE Internet of Things Journal, 2017, 5(1): 28–43. doi: 10.1109/JIOT.2017.2769446
    OBELE B O, IFTIKHAR M, MANIPORNSUT S, et al. Analysis of the behavior of self-similar traffic in a QoS-aware architecture for integrating WiMAX and GEPON[J]. Journal of Optical Communication and Network, 2009, 1(4): 259–273. doi: 10.1364/JOCN.1.000259
    MILAN G, JUAN E S, and JAMETT M. A simple estimator of the Hurst exponent for self-similar traffic flows[J]. IEEE Latin America Transactions, 2015, 12(8): 1349–1354. doi: 10.1109/TLA.2014.7014500
    TRAN T D and LE L B. Stackelberg game approach for wireless virtualization design in wireless networks[C]. 2017 IEEE International Conference on Communications (ICC), Paris, France, 2017: 1–6.
    WANG Cong, WANG Cuirong, and YUAN Ying. Game based dynamical bandwidth allocation model for virtual networks[C]. 2009 First International Conference on Information Science and Engineering, Nanjing, China, 2009: 1745–1747.
    LUONG N C, HOANG D T, WANG Ping, et al. Data collection and wireless communication in Internet of Things (IoT) using economic analysis and pricing models: A survey[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4): 2546–2590. doi: 10.1109/COMST.2016.2582841
    AL-ZAHRANI A Y and YU F R. An energy-efficient resource allocation and interference management scheme in green heterogeneous networks using game theory[J]. IEEE Transactions on Vehicular Technology, 2016, 65(7): 5384–5396. doi: 10.1109/TVT.2015.2464322
    XU Qichao, SU Zhou, and GUO Song. A game theoretical incentive scheme for relay selection services in mobile social networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(8): 6692–6702. doi: 10.1109/TVT.2015.2472289
    GHOSH A, COTTATELLUCCI L, and ALTMAN E. Normalized Nash equilibrium for power allocation in cognitive radio Networks[J]. IEEE Transactions on Cognitive Communications and Networking, 2015, 1(1): 86–99. doi: 10.1109/TCCN.2015.2496578
    RAO M S S and SOMAN S A. Marginal pricing of transmission services using min-max fairness policy[J]. IEEE Transactions on Power Systems, 2015, 30(2): 573–584. doi: 10.1109/TPWRS.2014.2331424
    ZHANG Yueyue, ZHU Yaping, YAN Feng, et al. Energy-efficient radio resource allocation in software-defined wireless sensor networks[J]. IET Communications, 2018, 12(3): 349–358. doi: 10.1049/iet-com.2017.0937
  • 加载中
图(7) / 表(1)
计量
  • 文章访问数:  1692
  • HTML全文浏览量:  1053
  • PDF下载量:  132
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-03-23
  • 修回日期:  2018-07-25
  • 网络出版日期:  2018-08-06
  • 刊出日期:  2019-02-01

目录

    /

    返回文章
    返回