Sparse Random Reconstruction of Data Loss With Low Redundancy in Wireless Sensor Networks for Mechanical Vibration Monitoring

Abstract

Wireless sensor networks (WSNs) for condition monitoring of mechanical equipment have been shown effective for ensuring operational safety and reducing breakdown losses. For battery-supply sensor nodes, energy efficiency for data transmission of mass vibration signals has become a significant challenge. In this article, an efficient low redundant data method is designed and implemented in the WSN nodes for reliable transmission. The proposed method includes two stages. Stage 1 involves the delta-discrete cosine transform (delta-DCT) that reduces the temporal and frequency redundancy of the original data so that the transferred data can be downsized via range coding. In stage 2, the sparse random reconstruction is injected to increase the redundancy of the compressed data to guarantee reliable transmission. Experimental results demonstrate that the original data can be downsized to 50%–55%, and the compression data can be recovered via sparse reconstruction without any data loss.