物联网中适用于内容搜索的实体状态匹配预测方法

张普宁; 刘元安; 吴帆; 唐碧华; 吴超

doi:10.11999/JEIT150191

物联网中适用于内容搜索的实体状态匹配预测方法

doi: 10.11999/JEIT150191

基金项目:

国家自然科学基金(61170275)，国家863计划项目，民用航天十二五预研科技项目，北京市高等学校青年英才计划项目和安全生产智能监控北京市重点实验室基金

计量
- 文章访问数: 1341
- HTML全文浏览量: 162
- PDF下载量: 575
- 被引次数: 0
出版历程
- 收稿日期: 2015-02-02
- 修回日期: 2015-08-19
- 刊出日期: 2015-12-19

An Entity State Matching Prediction Method for Content-basedSearch in the Internet of Things

Funds:

The National Natural Science Foundation of China (61170275)

摘要

摘要: 对实体匹配用户内容搜索的状态进行预测可显著提高物联网搜索的效率，降低搜索过程的通信开销。该文提出等时距与周期内实体状态预测方法，估计实体在用户查询时刻的状态；设计了适用于内容搜索的有序验证方法，依据实体匹配用户查询内容的概率对实体进行排序验证，以保证用户搜索结果的可靠性。结果表明，所提实体状态预测方法具有较高的精度，结合所提预测方法与匹配验证方法的搜索机制具有较低的通信开销。
- 物联网 /
- 内容搜索 /
- 状态预测 /
- 匹配验证
Abstract: Matching prediction with high accuracy of entity state can cansignificantly improve the efficiency of content-based search in the Internet of Things and reduce communication overhead while searching. The equal-interval and during the period entity state prediction methods are proposed, which are applied to the estimation of the entity state at the moment of querying. Moreover, the ordered verification approach is designed to verify the entities in sequence based on the degree of compliance with the searching content, for the sake of enhancing the reliability of searching results. Numerical results show that the proposed entity state prediction approachescan achieve high accuracy, which combines with the ordered verification approach to dramatically improve the performance of communication overheadduring the searching process.
- Internet of Things /
- Content-based search /
- State prediction /
- Matching verification

HTML全文

参考文献(16)

于海宁, 余翔湛. 物联网中基于编码的多路径并发实体通用搜索算法[J]. 智能计算机与应用, 2013, 3(2): 25-31.

Yu Hai-ning and Yu Xiang-zhan. A multi-path concurrency object universal search (MCOUS) algorithms based on code in internet of things[J]. Intelligent Computerand Applications, 2013, 3(2): 25-31.

茹立云, 李智超, 马少平. 搜索引擎索引网页集合选取方法研究[J]. 计算机研究与发展, 2014, 51(10): 2239-2247.

Ru Li-yun, Li Zhi-chao, and Ma Shao-ping. Indexing page collection selection method for search engine[J]. Journal of Computer Research and Development, 2014, 51(10): 2239-2247.

李养群, 沈苏彬, 许斌. 物品万维网技术综述[J].南京邮电大学学报(自然科学版), 2014, 34(2): 32-42.

Li Yang-qun, Shen Su-bin, and Xu Bin. Technology of web of things: asurvey[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2014, 34(2): 32-42.

于海宁, 张宏莉, 方滨兴, 等. 物联网中物理实体搜索服务的研究[J]. 电信科学, 2012, 28(10): 111-119.

Yu Hai-ning, Zhang Hong-li, Fang Bin-xing, et al.. Research on search service for physical entities in the internet of things[J]. Telecommunications Science, 2012, 28(10): 111-119.

Benedikt O, Kay R, Friedemann M, et al.. A real-time search engine for the web of things[C]. Internet of Things, Tokyo, 2010: 1-8.

Cuong T and Kay R. Content-based sensor search for the web of things[C]. Global Communications Conference, Atlanta, 2013: 2654-2660.

唐舟进, 彭涛, 王文博. 一种基于相关分析的局域最小二乘支持向量机小尺度网络流量预测算法[J]. 物理学报, 2014, 63(13): 57-66.

Tang Zhou-jin, Peng Tao, and Wang Wen-bo. A local least square support vector machine prediction algorithm of small scale network traffic based on correlation analysis[J]. Acta Physica Sinica, 2014, 63(13): 57-66.

李增科, 高井祥, 王坚, 等. 利用牛顿插值的GPS/INS组合导航惯性动力学模型[J]. 武汉大学学报(信息科学版), 2014, 39(5): 591-595.

Li Zeng-ke, Gao Jing-xiang, Wang Jian, et al.. Inerialdynamic model of GPS/INS integrated navigation based on newton interpolaion[J]. Geomatics and Information Science of Wuhan University, 2014, 39(5): 591-595.

尚军亮, 方敏. 一种优化的高精度灰色GM(1,1)预测模型[J]. 电子与信息学报, 2010, 32(6): 1301-1305.

Shang Jun-liang and Fang Min. New optimized method of high-precision grey GM(1,1) forecasting model[J]. Journal of Electronics Information Technology, 2010, 32(6): 1301-1305.

施引文献

资源附件(0)

访问统计