亢旭源,,
刘宇哲,
李学芳,
吴大鹏,
王汝言
1.重庆邮电大学通信与信息工程学院 重庆 400065
2.重庆高校市级光通信与网络重点实验室 重庆 400065
3.泛在感知与互联重庆市重点实验室 重庆 400065
基金项目:国家自然科学基金(61871062, 61901071)，重庆市高校创新团队建设计划资助项目(CXTDX201601020)，重庆市自然科学基金面上项目(cstc2019jcyj-msxmX0303)，重庆市教委科学技术研究项目(KJQN201800615)，第五批重庆市高校优秀人才支持计划(渝教人发[2017]29号)
详细信息
作者简介:张普宁：男，1988年生，博士，研究方向为物联网搜索
亢旭源：男，1991年生，硕士生，研究方向为物联网搜索
刘宇哲：男，1995年生，硕士生，研究方向为物联网搜索
李学芳：女，1995年生，硕士生，研究方向为物联网搜索
吴大鹏：男，1979年生，教授，研究方向为泛在无线网络、社会计算、互联网服务质量控制等
王汝言：男，1969年生，教授，研究方向为泛在网络、全光网络理论与技术、多媒体信息处理等
通讯作者:亢旭源 kangxuyuan163@163.com
中图分类号:TN915; TP393计量
文章访问数:1258
HTML全文浏览量:336
PDF下载量:35
被引次数:0
出版历程
收稿日期:2019-07-18
修回日期:2020-03-07
网络出版日期:2020-04-11
刊出日期:2020-07-23
Efficient Search Method for IoT Entities with Similarity Adaptive Estimation
Puning ZHANG,Xuyuan KANG,,
Yuzhe LIU,
Xuefang LI,
Dapeng WU,
Ruyan WANG
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Optical Communication and Networks Key Laboratory of Chongqing, Chongqing 400065, China
3. Ubiquitous Sensing and Networking Key Laboratory of Chongqing, Chongqing 400065, China
Funds:The National Natural Science Foundation (61871062, 61901071), The Program for Innovation Team Building at Institutions of Higher Education in Chongqing (CXTDX201601020), The General Project of Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0303), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800615), The Fifth Supporting Plan for Chongqing's University Excellent Talents (Chongqing Municipal Education Commission, No.29 [2017])
摘要
摘要:针对现有相似实体搜索方法缺乏对于观测序列长度的自适应性，且搜索过程数据存储开销过大，搜索结果准确性较低的问题，该文提出相似度自适应估计的物联网实体高效搜索方法(SAEES)。首先，设计了轻量级观测序列分段表示方法，对传感器采集的实体原始观测序列进行轻量级分段压缩表示，以降低实体观测序列的存储开销。然后，提出了观测序列相似度自适应估计方法，实现对不同观测序列长度的实体相似性的准确估计。最后，设计了高效的相似实体搜索匹配方法，依据所估计的实体相似度进行实体的准确搜索匹配。仿真结果表明，所提方法可大幅提高相似实体搜索的效率。
关键词:物联网/
实体搜索/
相似度/
自适应估计
Abstract:The existing similar entity search method has poor adaptability to the length of the observed sequence, and the data storage overhead in the search process is too large, and the accuracy of the search result is insufficient. To this end, an efficient search method is proposed for the IoT Entity Search with Similarity Adaptive Estimation (SAEES). Firstly, in order to reduce the storage overhead of the entity observation sequence, a lightweight method of segmentation representation of the observation sequence is designed to perform a lightweight segmentation compression representation of the original observation sequence of the entity collected by the sensor. Then, in order to achieve an accurate estimation of the similarity of entities with different observation sequence lengths, an adaptive estimation method for observation sequence similarity is proposed. Finally, by exploiting the designed efficient similar entity search matching method, the exact search matching of the entity is completed according to the estimated entity similarity. The simulation results show that the proposed method can greatly improve the efficiency of similar entity search.
Key words:Internet of Things (IoT)/
Entity search/
Similarity/
Adaptive estimation
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