WSNs Data Compression Algorithm Based on Compressed Sensing

摘要

According to wireless sensor network monitoring object features, the compressed sensing theory is applied to data compression to reduce the communication energy. Considering that reconstruction accuracy of existing data reconstruction in compressed sensing can be easily influenced by sparsity, after analysis of compressed sensing data reconstruction principle, with sub-frame processing the original signal in fixed length to reduce the solution space, and applying quantum theory encoding in Particle Swarm Optimization, Compressed Sensing Data Reconstruction that based on Quantum-behaved Particle Swarm Optimization appears. This algorithm improves the accuracy of the data reconstruction by improving particle initial position and update mode in Particle Swarm Optimization from Statistics. Simulation results show that under conditions of sparsity less than 50, QP-CSDR gets 20%-40% performance improvement on reconstruction accuracy comparing to existing algorithms. Now the algorithm has been applied to micro-earthquakes and audio monitoring system, and in actual inspection, the actual system life is extended about 2-4 times with assurance data accuracy.