Effective Quality-Aware Sensor Data Management

被引:20
作者
D'Aniello, Giuseppe [1 ]
Gaeta, Matteo [1 ]
Tzung-Pei Hong [2 ]
机构
[1] Univ Salerno, Dept Informat & Elect Engn & Appl Math, I-84084 Fisciano, Italy
[2] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung 811, Taiwan
来源
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE | 2018年 / 2卷 / 01期
关键词
Virtual Sensors; association rule mining; data imputation; semantic technologies; ASSOCIATION RULES; MISSING DATA; COMPRESSION; NETWORK;
D O I
10.1109/TETCI.2017.2782800
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Sensor networks are widely used for heterogeneous applications in diverse domains. One sensor network may provide data for applications and services that often have different or even competing requirements on the quality of the gathered sensor data; for example, the level of precision requested, the time constraints for getting the data, the level of accuracy, and so on. Providing the required level of quality for all applications and users is difficult, hindered by potential sensor malfunctions, communication problems, tampering, environmental conditions, and so on. Here, we propose a quality-aware sensor data management framework, which allows different users to define their own quality requirements by using a "virtual" sensor. These virtual sensors attempt to provide users with sensor data that satisfies their requests. For missing sensor readings or low quality data, association rule mining is used to estimate missing values, thus, improving users' perceived quality. Experiments conducted on a real sensor dataset show promising results in the estimation of missing sensor readings, compared to other data imputation techniques, when a significant spatio-temporal correlation among sensors exists.
引用
收藏
页码:65 / 77
页数:13
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