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基于计算区域网的智慧学习模型

A smart learning basing on computing area network

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【作者】 郭本俊王艳文立玉卢军

【Author】 GUO Ben-Jun,WANG Yan,WEN Li-Yu,LU Jun(Software Engineering College,Chengdu University of Information Technology,Chengdu 610225,China)

【机构】 成都信息工程学院软件工程学院

【摘要】 物联网的出现和快速发展,物体普遍联网对普适计算环境的服务迁移提出了更高的普遍适应要求,并将物联网普适计算转向于智能服务领域,移动学习作为智能服务的延伸是一种智慧学习.本文在动态虚拟重组的"小粒度"计算区域网基础上提出智慧学习模型:①智慧空间SLS,能有效地支撑普适计算的服务发现、执行评估和服务推送;②学习服务迁移模型SMCAN实现DCS(数据、代码、状态)的动态计算服务迁移.本文的主要贡献在于:①物联网以更小粒度,可热插拔服务,动态重组虚拟系统实现服务预测、服务执行能力评估和服务匹配;②智慧学习空间和SMCAN迁移模型将计算区域网作为核心点和制高点,为物联网普适计算智能服务研究提出新的思路.

【Abstract】 With the emergence and rapid development of the Internet of Things,at present,objects universally linked with the network,which raised more generally adapt to the requirements of the service migration on Ubiquitous computing environment.And the Internet of Things Pervasive Computing shift to the field of intelligent services.And,as an extension of the intelligent services,mobile learning is kind of smart-learning.This paper puts forward a This paper puts forward a smart-learning model,which is based on the dynamic virtual reorganization of the"small size" calculation area network: ① Smart-learning space(SLS),which can effectively support the Pervasive Computing in the areas of the service discovery,assessment implementation and services push.② Learning service migration model(SMCAN)realize the dynamic-computing-services migration of DCS(data,code,state).the main contribution of this paper is that:①Internet of Things adopts fine-grained,hot-swappable service and the dynamic-reorganization-virtual system to realize service-forecast,service execution capability assessment and service matching;②The SLS and SMCAN migration model use computing area network as the core point and the commanding point,and provide new ideas for the research on pervasive-computing intelligent-service of the Internet of Things.

【基金】 国家自然科学基金(61102076)
  • 【文献出处】 四川大学学报(自然科学版) ,Journal of Sichuan University(Natural Science Edition) , 编辑部邮箱 ,2013年04期
  • 【分类号】TP393.01
  • 【被引频次】1
  • 【下载频次】184
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