원문정보
초록
영어
Energy consumption is a critical problem affecting the lifetime of wireless sensor networks (WSNs) in structural health monitoring (SHM). A huge original acquisition data was transmitted between nodes which occupy a large amount of communication bandwidth, and even lead to paralysis of WSNs. Thus, data compression to reduce network traffic and energy loss before transmission is necessary. A number of traditional techniques have proposed to solve this issue by sampling the full signal and then taking compression process. But it spends a lot of processing time. In this paper, we establish suitability compressive sensing (CS) to address some challenges using WSN. Through the improvement of reconstruction algorithm and the experimental demonstration, the application of this method could ensure the accuracy of the data as well as balance the network energy consumption. Moreover, it can also reduce the cost of data storage and transmission which makes a certain contribution to the quality for SHM.
목차
1. Introduction
2. Compressive Sensing Theory
2.1 Signal Sparsity and the Sparse Representation
2.2 Measurement Matrix Selections in CS
2.3 Reconstruction Algorithms Selection in CS
2.4. Improved OMP Algorithm (TOMP)
3. Performance Evaluation
3.1 Evaluation Standards for CS Application in SHM
3.2 Simulation Results
4. Conclusions and Future Works
Acknowledgments
References