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Research on social relations cognitive model of mobile nodes in Internet of Things

机译:物联网中移动节点的社会关系认知模型研究

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摘要

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT). With the powerful sensing capability of smart device and human mobility, various services could be provided by building a trusted chain between service requesters and suppliers. The cognition of social relations between mobile nodes is the basis of final mobile-aware services. It involves many decision factors, such as time, space and activity patterns. Using social network theory, a new cognitive model for social relations of mobile nodes in loT is proposed. Firstly, nodes' social relations are reasoned and quantified from multiple perspectives based on the summary of social characteristics of mobile nodes and the definition of different decision factors. Then the location factor, interconnection factor, service evaluation factor and feedback aggregation factor are defined to solve the shortcomings in existing quantitative models. Finally, the weight distribution is set up by information entropy and rough set theory for these decision factors; it can overcome the shortage of traditional methods, in which the weight is set up by subjective ways and hence their dynamic adaptability is poor. We compare our cognitive model to existing models using MIT dataset by defining a variety of test indicators, such as network overall density (NOD), the degree center potential (DCP), the network distribution index (EI), etc. Simulation results show that, the cognitive model has better internal structure and significant validity in network analysis, and thus can provide mobile-aware service effectively in dynamic environment.
机译:人与智能移动设备之间的交互和通信是物联网(IoT)发展的新趋势。凭借智能设备的强大感知能力和人类移动性,可以通过在服务请求者和供应商之间建立信任的链来提供各种服务。对移动节点之间的社会关系的认识是最终的移动感知服务的基础。它涉及许多决策因素,例如时间,空间和活动方式。利用社交网络理论,提出了loT中移动节点社交关系的一种新的认知模型。首先,基于对移动节点社会特征的总结和不同决策因素的定义,从多个角度对节点的社会关系进行推理和量化。然后定义位置因子,互连因子,服务评估因子和反馈聚集因子,以解决现有定量模型中的缺点。最后,利用信息熵和粗糙集理论对这些决策因素建立权重分布。它可以克服传统方法的不足,传统方法的缺点是通过主观方法来设置权重,因此其动态适应性较差。通过定义各种测试指标,例如网络总体密度(NOD),度中心电位(DCP),网络分布指数(EI)等,我们将使用MIT数据集的认知模型与现有模型进行比较。仿真结果表明,认知模型在网络分析中具有较好的内部结构和显着的有效性,因此可以在动态环境中有效地提供移动感知服务。

著录项

  • 来源
    《Journal of network and computer applications》 |2013年第2期|799-810|共12页
  • 作者单位

    Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China,The Key Laboratory of Computer Network, Xi'an 710049, China;

    Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China,The Key Laboratory of Computer Network, Xi'an 710049, China;

    Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China,The Key Laboratory of Computer Network, Xi'an 710049, China;

    Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China,The Key Laboratory of Computer Network, Xi'an 710049, China;

    Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China,The Key Laboratory of Computer Network, Xi'an 710049, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    internet of things; mobile-aware; social relations; cognitive model; decision factors;

    机译:物联网;移动感知;社会关系;认知模型决定因素;

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