面向物联网的边云协同实体搜索方法

doi:10.19678/j.issn.1000-3428.0055501

计算机工程 ›› 2020, Vol. 46 ›› Issue (8): 43-49. doi: 10.19678/j.issn.1000-3428.0055501

面向物联网的边云协同实体搜索方法

王汝言^1,2,3, 刘宇哲^1,2,3, 张普宁^1,2,3, 亢旭源^1,2,3, 李学芳^1,2,3

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

收稿日期:2019-07-16 修回日期:2019-09-12 发布日期:2019-09-27
作者简介:王汝言(1969-),男,教授、博士,主研方向为泛在网络、多媒体信息处理;刘宇哲(通信作者),硕士研究生;张普宁,讲师、博士;亢旭源、李学芳,硕士研究生。
基金资助:
国家自然科学基金（61871062，61901071）；重庆市高校创新团队建设计划（CXTDX201601020）；重庆市自然科学基金面上项目（cstc2019jcyj-msxm1238）；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）。

Edge and Cloud Collaborative Entity Search Method for Internet of Things

WANG Ruyan^1,2,3, LIU Yuzhe^1,2,3, ZHANG Puning^1,2,3, KANG Xuyuan^1,2,3, LI Xuefang^1,2,3

1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. Chongqing Key Laboratory of Optical Communication and Network, Chongqing 400065, China;
3. Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Received:2019-07-16 Revised:2019-09-12 Published:2019-09-27

摘要/Abstract

摘要： 针对物联网搜索的高实时性要求及物理实体的强时变性特点，提出面向物联网的边云协同实体搜索方法。结合云计算与边缘计算各自的优势，构建边云协同实体搜索系统架构，提高物联网实体的搜索效率。考虑到嵌入物理实体的传感器通信能力有限，设计基于深度信念网络的实体识别算法，通过将热门与冷门实体状态信息分别存储于边缘服务器与云端，节省边缘服务器的存储空间与计算开销。仿真结果表明，与云端数据共享搜索方法SeDaSC和层次化搜索方法LHPM相比，该方法提高了实体状态信息搜索的实时性与准确性。

关键词: 物联网搜索, 边缘计算, 实体识别, 实体搜索, 边云协同

Abstract: To address high demands for real-time performance of search of Internet of Things(IoT) entities and the time-varying feature of physical entities,this paper proposes an Edge and Cloud Collaborative Entity Search Method(ECCS) for IoT.The method takes advantages of edge computing and cloud computing to construct entity search system architecture based on edge and cloud collaboration,so as to improve the search efficiency of IoT entities.Furthermore,to address limited communication capabilities of sensors of embedded physical entities,an entity identification algorithm based on Deep Belief Network(DBN) is proposed,which stores the status information of popular entities and unpopular entities respectively on the edge server and cloud to reduce the storage and computing cost of the edge server.Simulation results demonstrate that the proposed method can effectively improve the real-time performance and accuracy of search of entity status information compared with the cloud data sharing search method(SeDaSC) and the hierarchical search method(LHPM).

Key words: Internet of Things(IoT) search, edge computing, entity identification, entity search, edge and cloud collaboration

中图分类号:

TP393

王汝言, 刘宇哲, 张普宁, 亢旭源, 李学芳. 面向物联网的边云协同实体搜索方法[J]. 计算机工程, 2020, 46(8): 43-49.

WANG Ruyan, LIU Yuzhe, ZHANG Puning, KANG Xuyuan, LI Xuefang. Edge and Cloud Collaborative Entity Search Method for Internet of Things[J]. Computer Engineering, 2020, 46(8): 43-49.

https://www.ecice06.com/CN/Y2020/V46/I8/43

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参考文献

[1] ZHANG Punig,KANG Xuyuan,LI Xuefang,et al.Overlapping community deep exploring based relay selection method towards multi-hop D2D communication[EB/OL].[2019-05-13].https://www.researchgate.net/publication/333254850_Overlapping_Community_Deep_Exploring-Based_Relay_Selection_Method_Toward_Multi-Hop_D2D_Communication.
[2] GAO Yunquan,LI Xiaoyong,FANG Binxing.Survey on the search of Internet of things[J].Journal on Communications,2017,36(12):57-76.(in Chinese)高云全,李小勇,方滨兴.物联网搜索技术综述[J].通信学报,2017,36(12):57-76.
[3] ZHANG Punig,KANG Xuyuan,LI Xuefang,et al.Cooperative willingness aware collaborative caching mechanism towards cellular D2D communication[J].IEEE Access,2018,6:67046-67056.
[4] MA Huadong,LIU Wu.A progressive search paradigm for the Internet of things[J].IEEE Multimedia,2018,25(1):76-86.
[5] 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.
[6] ZHANG Puning,LIU Yuanan,WU Fan,et al.An entity state matching prediction method for content-based search in the Internet of things[J].Journal of Electronics and Information Technology,2015,37(12):2815-2820.(in Chinese)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.
[7] WU Dapeng,LIU Qianru,WANG Honggang,et al.Cache less for more:exploiting cooperative video caching and delivery in D2D communications[J].IEEE Transactions on Multimedia,2019,21(7):1788-1798.
[8] WU Dapeng,DENG Lingli,WANG Honggang,et al.Multimedia data transmission mechanism with privacy protection for Internet of vehicles[J].Future Generation Computer Systems,2019,99:609-623.
[9] 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.
[10] WU Dapeng,ZHANG Puning,WANG Ruyan.Smart Internet of things aided by "terminal-edge-cloud" cooperation[J].Chinese Journal on Internet of Things,2018,2(3):21-28.(in Chinese)吴大鹏,张普宁,王汝言."端-边-云"协同的智慧物联网[J].物联网学报,2018,2(3):21-28.
[11] HOU Lu,ZHAO Shaohang,XIONG Xiong,et al.Internet of things cloud:architecture and implementation[J].IEEE Communications Magazine,2016,54(12):32-39.
[12] ZHOU Y,DE S,WANG W,et al.Enabling query of frequently updated data from mobile sensing sources[C]//Proceedings of the 17th International Conference on Computational Science and Engineering.Washington D.C.,USA:IEEE Press,2014:946-952.
[13] MICHEL J,JULIEN C.A cloudlet-based proximal discovery service for machine-to-machine applications[C]//Proceedings of International Conference on Mobile Computing,Applica-tions,and Services.Berlin,Germany:Springer,2013:215-232.
[14] ALI M,DHAMOTHARAN R,KHAN E,et al.SeDaSC:secure data sharing in clouds[J].IEEE Systems Journal,2015,11(2):395-404.
[15] ZHANG P,LIU Y,WU F,et al.Low-overhead and high-precision prediction model for content-based sensor search in the Internet of things[J].IEEE Communications Letters,2016,20(4):720-723.
[16] ZHANG Puning,LIU Yuanan,WU Fan,et al.Content-based entity search with a matching estimation approach for Internet of things[J].Journal of Shanghai Jiao Tong University,2016,50(7):1060-1064,1070.(in Chinese)张普宁,刘元安,吴帆,等.带有匹配估计方法物联网基于内容的实体搜索机制[J].上海交通大学学报,2016,50(7):1060-1064,1070.
[17] SHI Weisong,SUN Hui,CAO Jie,et al.Edge computing-an emerging computing model for the Internet of everything era[J].Journal of Computer Research and Development,2017,54(5):907-924.(in Chinese)施巍松,孙辉,曹杰,等.边缘计算:万物互联时代新型计算模型[J].计算机研究与发展,2017,54(5):907-924.
[18] MOLLAH M B,AZAD M A K,VASILAKOS A.Secure data sharing and searching at the edge of cloud-assisted Internet of things[J].IEEE Cloud Computing,2017,4(1):34-42.
[19] LI H,OTA K,DONG M.Learning IoT in edge:deep learning for the Internet of things with edge computing[J].IEEE Network,2018,32(1):96-101.
[20] HOSSAIN S K A,RAHMAN M A,HOSSAIN M A.Edge computing framework for enabling situation awareness in IoT based smart city[J].Journal of Parallel and Distributed Computing,2018,122:226-237.
[21] WANG Lei,JIANG Lingyun,CHEN Xiaoyang,et al.Searching method for entity in Internet of things based on clustering[J].Journal of Nanjing University of Posts and Telecommunications(Natural Science),2017,37(3):113-118.(in Chinese)王蕾,江凌云,陈晓阳,等.一种基于聚类划分的IoT实体搜索方法[J].南京邮电大学学报(自然科学版),2017,37(3):113-118.
[22] JIN Peng,XIA Xiaofeng,QIAO Yan,et al.High-dimensional sensor data anomaly detection algorithm based on deep belief network[J].Chinese Journal of Sensors and Actuators,2019,32(6):892-901.(in Chinese)金鹏,夏晓峰,乔焰,等.基于深度信念网络的高维传感器数据异常检测算法[J].传感技术学报,2019,32(6):892-901.
[23] ZHOU Lijuan,WANG Hui,WANG Wenbo,et al.Parallel KMeans algorithm for massive data[J].Journal of Huazhong University of Science and Technology(Natural Science Edition),2012,40(S1):150-152.(in Chinese)周丽娟,王慧,王文伯,等.面向海量数据的并行KMeans算法[J].华中科技大学学报(自然科学版),2012,40(S1):150-152.
[24] ZHANG Jian,DING Shifei,ZHANG Nan,et al.Restricted Boltzmann machines:a review[J].Journal of Software,2019,30(7):2073-2090.(in Chinese)张健,丁世飞,张楠,等.受限玻尔兹曼机研究综述[J].软件学报,2019,30(7):2073-2090.
[25] Intel labdata[EB/OL].[2019-05-13].http://db.csail.mit.edu/labdata/labdata.html.

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