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.
张普宁,刘元安,吴帆,唐碧华,吴超. 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.
Zhang Pu-ning, Liu Yuan-an, Wu Fan, Tang Bi-hua, Wu Chao. An Entity State Matching Prediction Method for Content-based Search in the Internet of Things. JEIT, 2015, 37(12): 2815-2820.
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.
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.
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.
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.
[5]
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.
[6]
Cuong T and Kay R. Content-based sensor search for the web of things[C]. Global Communications Conference, Atlanta, 2013: 2654-2660.
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.
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.
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.
