Probabilistic Sensing: Intelligence in Data Sampling
This addresses energy efficiency for sensor-based systems like seismic surveys, representing a novel paradigm rather than an incremental improvement.
The paper tackles the problem of energy-inefficient data sampling in sensors by introducing a probabilistic sensing paradigm that decides when to sample, achieving lossless data acquisition with 0.41% normalized mean squared error and 93% savings in active operation time and sample generation.
Extending the intelligence of sensors to the data-acquisition process - deciding whether to sample or not - can result in transformative energy-efficiency gains. However, making such a decision in a deterministic manner involves risk of losing information. Here we present a sensing paradigm that enables making such a decision in a probabilistic manner. The paradigm takes inspiration from the autonomous nervous system and employs a probabilistic neuron (p-neuron) driven by an analog feature extraction circuit. The response time of the system is on the order of microseconds, over-coming the sub-sampling-rate response time limit and enabling real-time intelligent autonomous activation of data-sampling. Validation experiments on active seismic survey data demonstrate lossless probabilistic data acquisition, with a normalized mean squared error of 0.41%, and 93% saving in the active operation time of the system and the number of generated samples.