A Subspace Approach to Sparse Sampling Based Data Gathering in Wireless Sensor Networks

Abstract

Data gathering is an essential concern in Wireless Sensor Networks (WSNs). This paper proposes an efficient data gathering method in clustered WSNs based on sparse sampling to reduce energy consumption and prolong the network lifetime. For data gathering scheme, we propose a method that can collect sparse sampled data in each time slot with a fixed percent of nodes remaining in sleep mode. For data reconstruction, a subspace approach is proposed to enforce an explicit low-rank constraint for data reconstruction from sparse sampled data. Subspace representing spatial distributions of the WSNs data can be estimated from previous reconstructed data. Incorporating total variation constraint, the proposed reconstruction method reconstructs current time slot data efficiently. The results of experiments indicate that the proposed method can reduce the energy consumption and prolong the network lifetime with satisfying recovery accuracy.

Missing and Corrupted Data Recovery in Wireless Sensor Networks Based on Weighted Robust Principal Component Analysis

“…Considering that the missing of row of the data matrix due to a broken node will greatly degrade the recovery accuracy, the matrix completion method [19] was proposed to utilize the interpolation technique for WSNs data recovery. In addition, in order to address the needs of real-time reconstruction of data in practical applications, the sliding window-based reconstruction approach [20,21] was proposed to achieve real-time data recovery.…”