In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to ine...In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWLS-SVM） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted LS-SVM （WLS-SVM） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.展开更多
一个好的时空数据库逻辑设计目标是消除数据冗余以及插入、删除和更新异常。因此,对时空函数依赖STFD(Spatio-Temporal Function Dependency),时空关键字,时空完全函数依赖进行了定义,在此基础上对时空数据库进行了规范化研究,提出了时...一个好的时空数据库逻辑设计目标是消除数据冗余以及插入、删除和更新异常。因此,对时空函数依赖STFD(Spatio-Temporal Function Dependency),时空关键字,时空完全函数依赖进行了定义,在此基础上对时空数据库进行了规范化研究,提出了时空一范式、时空二范式、时空三范式,并对它们的规范化程度的高低次序进行了证明。展开更多
基金Supported by Basic Research Foundation of Beijing Institute of Technology (20070542009)
文摘In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWLS-SVM） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted LS-SVM （WLS-SVM） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.
文摘一个好的时空数据库逻辑设计目标是消除数据冗余以及插入、删除和更新异常。因此,对时空函数依赖STFD(Spatio-Temporal Function Dependency),时空关键字,时空完全函数依赖进行了定义,在此基础上对时空数据库进行了规范化研究,提出了时空一范式、时空二范式、时空三范式,并对它们的规范化程度的高低次序进行了证明。
