A Comparison of On-Mote Lossy Compression Algorithms for Wireless Seismic Data Acquisition

Abstract

In this article, we rigorously compare compressive sampling (CS) to four state of the art, on-mote, lossy compression algorithms (K-run-length encoding (KRLE), lightweight temporal compression (LTC), wavelet quantization thresholding and run-length encoding (WQTR), and a low-pass filtered fast Fourier transform (FFT)). Specifically, we first simulate lossy compression on two real-world seismic data sets, and we then evaluate algorithm performance using implementations on real hardware. In terms of compression rates, recovered signal error, power consumption, and classification accuracy of a seismic event detection task (on decompressed signals), results show that CS performs comparable to (and in many cases better than) the other algorithms evaluated. The main benefit to users is that CS, a lightweight and non-adaptive compression technique, can guarantee a desired level of compression performance (and thus, radio usage and power consumption) without subjugating recovered signal quality. Our contribution is a novel and rigorous comparison of five state of the art, on-mote, lossy compression algorithms in simulation on real-world data sets and implemented on hardware.