A Data Loss Recovery Technique using Compressive Sensing for Structural Health Monitoring Applications

Recent developments in Wireless Sensor Networks (WSN) benefited various fields, among them Structural Health Monitoring (SHM) is an important application of WSNs. Using WSNs provides multiple advantages such as continuous monitoring of structure, lesser installation costs, fewer human inspections. However, because of the wireless medium, hardware faults, etc., data loss is an unavoidable consequence of WSNs. Recently, a new class of data loss recovery technique using Compressive Sensing (CS) is getting attention from the research community. In these methods, the transmitter sends encoded acceleration data and receiver uses a CS recovery method to recover the original signal. Usually, the encoding process uses a random measurement matrix which makes the process computationally complex to implement on sensor nodes. This paper presents a technique where the signal is encoded using Scrambled Identity Matrix. Using this method reduces the computational complexity and also robust to data loss. A performance analysis of the proposed technique is presented for random and continuous data loss. A comparison with the existing data loss recovery techniques is also shown using simulated data loss (both random and continuous data loss). It is observed that the proposed technique using Scrambled Identity Matrix can reconstruct the signals even after significant loss of data.