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中文摘要：
在无线传感器网络中,感知数据的缺失问题不可避免,并且给无线传感器网络的各种应用带来了巨大困难.解决该问题的最好办法是对缺失数据进行准确估计.文中首先提出了一种基于感知数据时间相关性的缺失值估计算法,该算法采用线性插值模型,能够对较短时间内平稳变化的感知数据的缺失值进行较好估计;其次,文中提出了一种基于感知数据空间相关性的缺失值估计算法,该算法采用多元回归模型,同时考察多个邻居节点并联合地用其感知数据来共同估计缺失值.该算法不仅能够对非平稳变化的感知数据的缺失值取得较好估计效果,而且在给出缺失数据估计值的同时,还能够对用户给定的置信度给出缺失值的置信区间;基于上述两种算法,文中最后给出了一种自适应的基于感知数据时-空相关性的缺失值估计算法.该算法无论对于平稳变化还是非平稳变化的感知数据的缺失值均能取得较好的估计效果.作者在真实的数据集合上对文中提出的算法进行了测试,实验结果证明文中提出的基于感知数据时-空相关性的缺失值估计算法能够有效估计无线传感器网络中的缺失数据,具有可靠、稳定的估计性能.
英文摘要：
In wireless sensor networks,the missing of the sensing data is inevitable due to the inherent characteristic of wireless sensor networks,and it causes many difficulties in various applications.To solve the problem,the best way is to estimate the missing values as accurately as possible.In this paper,a temporal correlation based missing values imputation algorithm is proposed firstly.It adopts linear interpolation model to estimate the missing values and a good esti-mation effect can be achieved for the sensing data changing smoothly in a short time. Next, a spatial correlation based missing values imputation algorithm is proposed. It adopts multiple regression model and estimates the missing values with the data of multiple neighbor nodes jointly rather than independently, so that it can achieve a good estimation effect even for the sensing data that changing non-smoothly. Besides, it can not only give the estimated values of the missing data, but also give the confidence interval of each missing data for the given confidence level. Based on these two algorithms, an adaptive temporal and spatial correlation based missing values imputation algorithm is proposed at the end of this paper. It performs well both for the sensing data changing smoothly and non-smoothly. Experimental results on a real-world dataset show that the proposed algorithms can estimate the missing values accurately.
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