电子学报 ›› 2016, Vol. 44 ›› Issue (5): 1055-1063.DOI: 10.3969/j.issn.0372-2112.2016.05.007

• 学术论文 • 上一篇    下一篇

一种物联网设备自动描述方法

李勐1,2, 王晓峰1, 崔莉1   

  1. 1. 中国科学院计算技术研究所, 北京 100190;
    2. 中国科学院大学, 北京 100049
  • 收稿日期:2014-07-22 修回日期:2015-07-24 出版日期:2016-05-25 发布日期:2016-05-25
  • 通讯作者: 崔莉
  • 作者简介:李勐 男,1989年出生,现为中科院计算技术研究所博士研究生.主要研究方向为物联网资源的发现与检索.E-mail:limeng@ict.ac.cn;王晓峰 男,1978年出生,博士,目前为中国科学院计算技术研究所助理研究员.主要研究方向为物联网大数据处理、智能移动计算、数据挖掘、人工智能.E-mail:wangxiaofeng@ict.ac.cn
  • 基金资助:

    中国科学院战略性先导科技专项(No.XDA06010403);国家国际科技合作专项(No.2013DFA10690);国家自然科学基金(No.61202211)

An Automatic Device Describe Method for Internet of Things

LI Meng1,2, WANG Xiao-feng1, CUI LI1   

  1. 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2014-07-22 Revised:2015-07-24 Online:2016-05-25 Published:2016-05-25

摘要:

物联网感知设备的服务描述文件为海量资源的发现与检索提供了有效的支持,是面向服务的物联网架构的基础.当前服务描述文件主要通过开发人员手工撰写完成,工作量大.现有研究SPITFIRE提出了一种半自动方法协助开发人员撰写服务描述文件,但方法本身为集中式方法,配置较复杂且精度过度依赖人工参数调优,不适合大规模部署.针对物联网海量设备的描述问题,本文提出了一种基于度量学习的分布式的物联网感知设备自动描述方法.该方法使用设备的多种数值特征作为输入,利用一种分布式的DBSCAN聚类算法对设备进行归类与推导,设备通过归类结果可自动生成自身描述文件.该方法利用度量学习优化聚类的度量函数以保障精度,以分布式方式进行灵活快速的配置,可减少人工干扰.仿真实验表明,与使用单一属性作为度量方式的SPITFIRE相比较,本文方法在获得对设备聚类相当的查全率的同时,查准率提高了20.4%,更适合于物联网海量设备使用场景.

关键词: 物联网, 海量设备, 描述文件, 分布式, 优化, 聚类

Abstract:

Service description file, which is fundamental for the service-oriented architecture, can be used by the IoT sensing devices to accelerate resource discovery and searching processes.Currently, these files are mostly written manually by the device developers, this process is inefficient and fallible.The state-of-art method SPITFIRE can generate the devices' description in a semi-automatic way, but its configuration can be trivial and its accuracy still can be improved.In this paper, we proposed a novel automatic device description method, with which devices can automatically generate their individual description.We designed a clustering algorithm based on DBSCAN to infer the description of sensing device, taking advantages of existing descriptions and the data features of series gained by data sampling.A metric learning algorithm is also implemented to optimize the parameter used by the clustering algorithm.All the routines run independently on different devices, and no manual intervention is needed during the self-description process.Through simulation, we show that this method has a prominent advantage of precision over other state-of-art methods, making our method more suitable for the massive IoT devices.

Key words: internet of things, massive device, description file, distributed, optimization, clustering

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