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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。提出了一种基于多元回归模型的缺失值估计算法。该算法首先依感知数据的时间相关性和空间相关性分别采用多元线性回归模型对缺失数据进行估计，然后根据回归模型的拟合优度对基于时间维和空间维求出的两个估计值分别赋予相应的权值系数，并将其加权平均值作为缺失数据的最后估计值。由于该算法在对缺失值进行估计时，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，因此该算法具有可靠、稳定的估计性能。在两个真实的数据集合上对该算法进行了测试，实验结果表明提出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。
英文摘要：
In wireless sensor network, the missing of sensor data is inevitable due to the inherent characteristic of wireless sensor network, and it causes many difficulties in various applications. To solve the problem, the best way is to estimate the missing data as accurately as possible. In this paper, a multiple-regression-model-based missing values imputation algorithm is proposed. It first adopts the multiple linear regression model to estimate the missing data both on temporal dimension and spatial dimension. Then, it assigns the weight coefficients to the two estimated values computed respectively on temporal dimension and spatial dimension according to the goodness-of-fit, and then uses the weighted average of the two values as the final estimated value. Since the algorithm estimates the missing data with the data of multiple neighbor nodes jointly rather than independently, its estimation performance is more stable and reliable. Experimental results on two real-world datasets show that the proposed algorithm can estimate the missing data accurately.
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