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基于半马尔科夫决策过程的虚拟传感网络资源分配策略

王汝言 李宏娟 吴大鹏 李红霞

引用本文: 王汝言, 李宏娟, 吴大鹏, 李红霞. 基于半马尔科夫决策过程的虚拟传感网络资源分配策略[J]. 电子与信息学报, 2019, 41(12): 3014-3021. doi: 10.11999/JEIT190016 shu
Citation:  Ruyan WANG, Hongjuan LI, Dapeng WU, Hongxia LI. Semi-Markov Decision Process-based Resource Allocation Strategy for Virtual Sensor Network[J]. Journal of Electronics and Information Technology, 2019, 41(12): 3014-3021. doi: 10.11999/JEIT190016 shu

基于半马尔科夫决策过程的虚拟传感网络资源分配策略

    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;
    李红霞: 女,1969年生,高级工程师,研究方向为光无线融合网络、无线传感网络
    通讯作者: 李宏娟,ilihj@foxmail.com
  • 基金项目: 国家自然科学基金(61871062,61771082),重庆市高校创新团队建设计划资助项目(CXTDX201601020)

摘要: 针对传统无线传感网络(WSN)中资源部署与特定任务的耦合关系密切,造成较低的资源利用率,进而给资源提供者带来较低的收益问题,根据虚拟传感网络请求(VSNR)的动态变化情况,该文提出虚拟传感网络(VSN)中基于半马尔科夫决策过程(SMDP)的资源分配策略。定义VSN的状态集、行为集、状态转移概率,考虑传感网能量受限以及完成VSNR的时间,给出奖赏函数的表达式,并使用免模型强化学习算法求解特定状态下的行为,从而最大化网络资源提供者的长期收益。数值结果表明,该文的资源分配策略能有效提高传感网资源提供者的收益。

English

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  • 图 1  不同${\lambda _{\rm{p}}}$的收益对比

    图 2  不同资源总量的收益对比图

    图 3  不同${\lambda _{\rm{p}}}$的收益对比图

    图 4  不同${\mu _{\rm{p}}}$的收益对比图

    图 5  不同${\lambda _{\rm{p}}}$的拒绝率

    表 1  仿真参数设置表

    参数数值参数数值
    $K$20~30${\lambda _{\rm{p}}}$1~18
    ${\omega _{\rm{e}}}$0.5${\omega _{\rm{d}}}$0.5
    ${\beta _{\rm{e}}}$2${\beta _{\rm{d}}}$2
    ${E_{\rm{l}}}$20${P_{\rm{l}}}$5
    ${D_{\rm{l}}}$20$\delta $2
    $\gamma $2$\alpha $0.1
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文章相关
  • 通讯作者:  李宏娟, ilihj@foxmail.com
  • 收稿日期:  2019-01-07
  • 录用日期:  2019-04-16
  • 网络出版日期:  2019-05-22
  • 刊出日期:  2019-12-01
通讯作者: 陈斌, bchen63@163.com
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