相似度自適應估計的物聯(lián)網(wǎng)實體高效搜索方法

張普寧; 亢旭源; 劉宇哲; 李學芳; 吳大鵬; 王汝言

doi:10.11999/JEIT190541

相似度自適應估計的物聯(lián)網(wǎng)實體高效搜索方法

doi: 10.11999/JEIT190541 cstr: 32379.14.JEIT190541

1.
重慶郵電大學通信與信息工程學院重慶 400065
2.
重慶高校市級光通信與網(wǎng)絡重點實驗室重慶 400065
3.
泛在感知與互聯(lián)重慶市重點實驗室重慶 400065

基金項目: 國家自然科學基金(61871062, 61901071)，重慶市高校創(chuàng)新團隊建設計劃資助項目(CXTDX201601020)，重慶市自然科學基金面上項目(cstc2019jcyj-msxmX0303)，重慶市教委科學技術研究項目(KJQN201800615)，第五批重慶市高校優(yōu)秀人才支持計劃(渝教人發(fā)[2017]29號)

詳細信息

作者簡介:
張普寧：男，1988年生，博士，研究方向為物聯(lián)網(wǎng)搜索

亢旭源：男，1991年生，碩士生，研究方向為物聯(lián)網(wǎng)搜索

劉宇哲：男，1995年生，碩士生，研究方向為物聯(lián)網(wǎng)搜索

李學芳：女，1995年生，碩士生，研究方向為物聯(lián)網(wǎng)搜索

吳大鵬：男，1979年生，教授，研究方向為泛在無線網(wǎng)絡、社會計算、互聯(lián)網(wǎng)服務質量控制等

王汝言：男，1969年生，教授，研究方向為泛在網(wǎng)絡、全光網(wǎng)絡理論與技術、多媒體信息處理等

通訊作者:
亢旭源　kangxuyuan163@163.com

中圖分類號: TN915; TP393
計量
- 文章訪問數(shù): 2246
- HTML全文瀏覽量: 587
- PDF下載量: 62
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-07-18
- 修回日期: 2020-03-07
- 網(wǎng)絡出版日期: 2020-04-11
- 刊出日期: 2020-07-23

Efficient Search Method for IoT Entities with Similarity Adaptive Estimation

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:
針對現(xiàn)有相似實體搜索方法缺乏對于觀測序列長度的自適應性，且搜索過程數(shù)據(jù)存儲開銷過大，搜索結果準確性較低的問題，該文提出相似度自適應估計的物聯(lián)網(wǎng)實體高效搜索方法(SAEES)。首先，設計了輕量級觀測序列分段表示方法，對傳感器采集的實體原始觀測序列進行輕量級分段壓縮表示，以降低實體觀測序列的存儲開銷。然后，提出了觀測序列相似度自適應估計方法，實現(xiàn)對不同觀測序列長度的實體相似性的準確估計。最后，設計了高效的相似實體搜索匹配方法，依據(jù)所估計的實體相似度進行實體的準確搜索匹配。仿真結果表明，所提方法可大幅提高相似實體搜索的效率。
- 物聯(lián)網(wǎng) /
- 實體搜索 /
- 相似度 /
- 自適應估計
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.
- Internet of Things (IoT) /
- Entity search /
- Similarity /
- Adaptive estimation

HTML全文

圖 1 相似實體搜索架構設計

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圖 2 不同分段數(shù)量下的擬合誤差

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圖 3 不同數(shù)據(jù)量下的分段時間消耗

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圖 4 不同相似度閾值下傳感器的查準率

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圖 5 不同分段數(shù)下的查準率

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圖 6 不同候選實體觀測序列長度下的查準率

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圖 7 不同基準實體觀測序列長度下的查準率

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