PANDA: Performance Acceleration through Nonuniform Data Acquisition

We present PANDA, a data acquisition technique for energy-constrained sensor nodes that reduces the energy per operation required to sample and preprocess analog sensor data. PANDA takes advantage of the energy consumption patterns of commodity microcontrollers by sampling input signals in short bursts followed by long periods of inactivity. This approach reduces the overhead of repetitively transitioning the CPU and analog components in and out of low-power sleep states. This nonuniformly-spaced input data is then fed to a nonuniform FFT algorithm that computes the frequency spectrum. We show that the spectrum computed with the nonuniform FFT is very close to the spectrum that would be computed from uniformly sampled data preprocessed with a conventional FFT. The output of the nonuniform FFT can be filtered or postprocessed with conventional frequency domain analysis techniques, and a uniformly resampled output can be constructed with the conventional inverse FFT. We compare the energy consumption patterns of burst-mode sampling to those of conventional uniform sampling in several real sensor nodes. We demonstrate that for reasonably sized input datasets, burst mode sampling and postprocessing consumes more than 17% less energy than conventional uniform sampling, including the additional computations required to compute the nonuniform DFT.