Probabilistic Sensing: Intelligence in Data Sampling

2026-01-27

AI Summary

This work proposes a novel intelligent sensing paradigm inspired by the autonomic nervous system to overcome the limitations of conventional sensors, which often suffer from information loss or excessive energy consumption due to their lack of intelligent decision-making in deterministic sampling. By integrating probabilistic neurons (p-neurons) with analog feature extraction circuits for the first time, the authors develop a hardware architecture capable of real-time, autonomous probabilistic sampling with microsecond-level response latency. This approach transcends the traditional trade-off between sub-Nyquist sampling rates and response speed, achieving significant energy savings without compromising information fidelity. Experimental validation in active seismic exploration demonstrates the system’s efficacy: it reduces both runtime and the number of samples by 93% while maintaining an exceptionally low normalized mean square error of only 0.41%, underscoring its practicality and efficiency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• probabilistic sensing
• data acquisition
• energy efficiency
• intelligent sampling
• information loss

Innovation

• Probabilistic Sensing
• p-neuron
• analog feature extraction
• energy-efficient sampling
• real-time autonomous sensing