Distributed Low Rank Compressed Sensing Utilized in a Structural Health Monitoring System

Structural Health Monitoring (SHM) Systems are utilized in critical and vital structures to identify the state of health in them. For the bridges in which SHM systems are widely used, Due to the complexity of structure, dense sensor networks are much more desirable. In literature it is proved that these dense sensor networks can only be implemented using Wireless Sensor Networks while these systems are faced with some challenging problems too. The large size of data being transmitted in such networks and high energy consumption regarding the data transmission, raise the question that how we can reduce the size of data to be transferred and consequently decrease the power dedicated to this task.We have introduced a method to compress large low rank dataset and an algorithm to implement the method in distributed manner. The method is able to cut up to 75% of the size of data which is a high rate of compression compared with the literature. Furthermore the performance of the data regeneration algorithm is studied both in presence and absence of measurement noise and finally the proof of large reduction in power consumption using this method is given mathematically.

Keywords:

Compressed-Sensing (CS) , Low Rank Compressed Sensing , Structural Health Monitoring (SHM) , Distributed algorithm , Wireless sensor Networks (WSN)