  With ubiquitous sensors continuously monitoring and collecting large amounts
of information, there is no doubt that this is an era of big data. One of the
important sources for scientific big data is the datasets collected by Internet
of things (IoT). It's considered that these datesets contain highly useful and
valuable information. For an IoT application to analyze big sensor data, it is
necessary that the data are clean and lossless. However, due to unreliable
wireless link or hardware failure in the nodes, data loss in IoT is very
common. To reconstruct the missing big sensor data, firstly, we propose an
algorithm based on matrix rank-minimization method. Then, we consider IoT with
multiple types of sensor in each node. Accounting for possible correlations
among multiple-attribute sensor data, we propose tensor-based methods to
estimate missing values. Moreover, effective solutions are proposed using the
alternating direction method of multipliers. Finally, we evaluate the
approaches using two real sensor datasets with two missing data-patterns, i.e.,
random missing pattern and consecutive missing pattern. The experiments with
real-world sensor data show the effectiveness of the proposed methods.
