相似度自适应估计的物联网实体高效搜索方法

张普宁; 亢旭源; 刘宇哲; 李学芳; 吴大鹏; 王汝言

doi:10.11999/JEIT190541

相似度自适应估计的物联网实体高效搜索方法

doi: 10.11999/JEIT190541

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
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- 文章访问数: 2110
- HTML全文浏览量: 521
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- 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

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。
- 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.
- Internet of Things (IoT) /
- Entity search /
- Similarity /
- Adaptive estimation

HTML全文

图 1 相似实体搜索架构设计

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图 2 不同分段数量下的拟合误差

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图 3 不同数据量下的分段时间消耗

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图 4 不同相似度阈值下传感器的查准率

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图 5 不同分段数下的查准率

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图 6 不同候选实体观测序列长度下的查准率

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图 7 不同基准实体观测序列长度下的查准率

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参考文献(21)

WU Dapeng, SHI Hang, WANG Honggang, et al. A feature-based learning system for internet of things applications[J]. IEEE Internet of Things Journal, 2019, 6(2): 1928–1937. doi: 10.1109/JIOT.2018.2884485

ZHANG Puning and MA Jie. Channel characteristic aware privacy protection mechanism in WBAN[J]. Sensors, 2018, 18(8): 2403. doi: 10.3390/s18082403

ZHANG Puning, KANG Xuyuan, WU Dapeng, et al. High-accuracy entity state prediction method based on deep belief network toward IoT search[J]. IEEE Wireless Communications Letters, 2019, 8(2): 492–495. doi: 10.1109/LWC.2018.2877639

ZHANG Puning, KANG Xuyuan, LIU Yuzhe, et al. Cooperative willingness aware collaborative caching mechanism towards cellular D2D communication[J]. IEEE Access, 2018, 6: 67046–67056. doi: 10.1109/ACCESS.2018.2873662

WU Dapeng, LIU Bingxu, YANG Qing, et al. Social-aware cooperative caching mechanism in mobile social networks[J]. Journal of Network and Computer Applications, 2020, 149: 102457. doi: 10.1016/j.jnca.2019.102457

高云全, 李小勇, 方滨兴. 物联网搜索技术综述[J]. 通信学报, 2015, 36(12): 57–76. doi: 10.11959/j.issn.1000-436x.2015315

GAO Yunquan, LI Xiaoyong, and FANG Binxing. Survey on the search of internet of things[J]. Journal on Communications, 2015, 36(12): 57–76. doi: 10.11959/j.issn.1000-436x.2015315

张普宁, 刘元安, 吴帆, 等. 物联网中适用于内容搜索的实体状态匹配预测方法[J]. 电子与信息学报, 2015, 37(12): 2815–2820. doi: 10.11999/JEIT150191

ZHANG Puning, LIU Yuan’an, WU Fan, et al. An entity state matching prediction method for content-based search in the internet of things[J]. Journal of Electronics &Information Technology, 2015, 37(12): 2815–2820. doi: 10.11999/JEIT150191

邹宇驰, 刘松, 于楠, 等. 基于搜索的物联网设备识别框架[J]. 信息安全学报, 2018, 3(4): 25–40. doi: 10.19363/J.cnki.cn10-1380/tn.2018.07.03

ZOU Yuchi, LIU Song, YU Nan, et al. IoT device recognition framework based on Web search[J]. Journal of Cyber Security, 2018, 3(4): 25–40. doi: 10.19363/J.cnki.cn10-1380/tn.2018.07.03

李强, 贾煜璇, 宋金珂, 等. 网络空间物联网信息搜索[J]. 信息安全学报, 2018, 3(5): 38–53.

LI Qiang, JIA Yuxuan, SONG Jinke, et al. Search of internet of thing information in the cyberspace[J]. Journal of Cyber Security, 2018, 3(5): 38–53.

MA Huadong and LIU Wu. A progressive search paradigm for the internet of things[J]. IEEE MultiMedia, 2018, 25(1): 76–86. doi: 10.1109/MMUL.2017.265091429

YAP K K, SRINIVASAN V, and MOTANI M. Max: Wide area human-centric search of the physical world[J]. ACM Transactions on Sensor Networks, 2008, 4(4): 26. doi: 10.1145/1387663.1387672

WANG Haodong, TAN C C, and LI Qun. Snoogle: A search engine for pervasive environments[J]. IEEE Transactions on Parallel and Distributed Systems, 2010, 21(8): 1188–1202. doi: 10.1109/TPDS.2009.145

TAN C C, SHENG Bo, WANG Haodong, et al. Microsearch: A search engine for embedded devices used in pervasive computing[J]. ACM Transactions on Embedded Computing Systems, 2010, 9(4): 43. doi: 10.1145/1721695.1721709

FATHY Y, BARNAGHI P, and TAFAZOLLI R. Large-scale indexing, discovery, and ranking for the Internet of Things (IoT)[J]. ACM Computing Surveys, 2018, 51(2): 29. doi: 10.1145/3154525

刘强强, 余黎青, 赵鹏, 等. 基于移动平台的图像检索系统[J]. 计算机技术与发展, 2016, 26(11): 10–13.

LIU Qiangqiang, YU Liqing, ZHAO Peng, et al. A novel image retrieval system based on mobile platform[J]. Computer Technology and Development, 2016, 26(11): 10–13.

TRUONG C, RÖMER K, and CHEN Kai. Fuzzy-based sensor search in the web of things[C]. The 3rd IEEE International Conference on the Internet of Things, Wuxi, China, 2012: 127–134. doi: 10.1109/IOT.2012.6402314.

刘素艳, 刘元安, 吴帆, 等. 物联网中基于相似性计算的传感器搜索[J]. 电子与信息学报, 2018, 40(12): 3020–3027.

LIU Suyan, LIU Yuanan, WU Fan, et al. Sensor search based on sensor similarity computing in the internet of things[J]. Journal of Electronics &Information Technology, 2018, 40(12): 3020–3027.

LI Zhidu, JIANG Yuming, GAO Yuehong, et al. On buffer-constrained throughput of a wireless-powered communication system[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(2): 283–297. doi: 10.1109/JSAC.2018.2872374

PATTAR S, BUYYA R, VENUGOPAL K R, et al. Searching for the IoT resources: Fundamentals, requirements, comprehensive review, and future directions[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3): 2101–2132. doi: 10.1109/COMST.2018.2825231

ZHANG Zufan, ZENG Tian, YU Xiulan, et al. Social-aware D2D pairing for cooperative video transmission using matching theory[J]. Mobile Networks and Applications, 2018, 23(3): 639–649. doi: 10.1007/s11036-017-0973-z

Intel Berkeley Research Lab. Intel berkeley research lab sensors data[EB/OL]. http://db.csail.mit.edu/labdata/labdata.html, 2004.

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