文章摘要
刘晓霞,周绍军.物联网智能感知节点基于π网软硬件划分模型研究[J].西南民族大学自然科学版,2018,44(1):56-63
物联网智能感知节点基于π网软硬件划分模型研究
Research on hardware/software partitioning model for intelligent sensing nodes of internet of things based on π-nets
投稿时间:2017-12-03  修订日期:2017-12-05
中文关键词: 物联网  智能感知节点  π网  软硬件划分.
英文关键词: internet of things  intelligent sensing node  π-net  hardware/software partitioning.
基金项目:国家科技计划支撑基金项目(2012BAH76F01),四川省水利厅2017年科研计划项目(SL2017-01)
作者单位E-mail
刘晓霞 四川水利职业技术学院 信息工程系 lxx0903@qq.com 
周绍军 四川水利职业技术学院 信息工程系  
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中文摘要:
      针对物联网智能感知节点的软硬件划分问题,提出了基于π网的物联网智能感知节点的软硬件划分模型,并对模型进行了实验与仿真。首先对物联网智能感知节点进行带约束定义,得到了智能感知节点的约束模型;然后利用π网理论,建立了基于π网的物联网智能感知节点软硬件划分模型,并对模型进行了演化分析;最后,利用先进Pareto优化算法对划分模型进行了优化,同时与TS禁忌搜索算法和GA遗传算法等进行了对比实验。通过分析与实验仿真,建立的模型,在适应度和划分执行时间等方面具有一定的优越性和实用性。
英文摘要:
      Aiming at the problem of hardware and software partition of intelligent sensing nodes in Internet of Things, a hardware and software partition model of intellisense nodes based on π network is proposed, experimented and simulated. Firstly, the constrained model of the intellisense node is obtained by defining intellisense nodes of the internet of things with constraints. Then, the hardware and software partition model of intelligent sensing nodes based on π network is established by using the π network theory, and the model is analyzed by evolution analysis method. Finally, the partition model is optimized by the advanced Pareto optimization algorithm, and compared with TS tabu search algorithm and GA genetic algorithm in experiments. Through the analysis and experimental simulation, the model established in this paper has certain superiority and practicability in the aspects of adaptability and division of execution time.
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