In this article, we introduce a novel on-mote compressive sampling method called the Randomized Timing Vector algorithm (RTV). In addition to describing this new lightweight algorithm, we provide experimental results that compare RTV to the two existing on-mote compressive sampling algorithms that we are aware: Additive Random Sampling (ARS) and Sparse Binary Sampling (SBS). Experimentation involved three different steps. First, we tested and validated the three on-mote compressive sampling algorithms using a simplistic sinusoid produced by a signal generator. Second, we analyzed the power consumption of the three algorithms and compared them to full sampling. Lastly, we simulated the three algorithms on a real-world passive seismic dataset containing avalanche events collected in the mountains of Switzerland. Results from our experiments indicate that our novel and lightweight RTV algorithm outperforms ARS and SBS in at least two ways. First, unlike ARS and SBS, RTV does not falter at moderate to high sampling rates (e.g., 500 Hz or above). Second, RTV showed the greatest power savings since it eliminates costly floating point calculations and reduces ADC conversions.
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