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Related Experiment Video

Updated: Nov 27, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Exploration vs. Data Refinement via Multiple Mobile Sensors.

Mohammad Shekaramiz1, Todd K Moon1, Jacob H Gunther1

  • 1Electrical and Computer Engineering Department and Information Dynamics Laboratory, Utah State University, 4120 Old Main Hill, Logan, UT 84322-4120, USA.

Entropy (Basel, Switzerland)
|December 3, 2020
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Summary
This summary is machine-generated.

This study introduces a novel framework for mobile sensors to map physical quantities in unknown areas. It balances discovering new regions with refining existing data for better measurements.

Keywords:
Gaussian process regression (GPR)adaptive samplingdata refinementdecision under conflictepistemic utility controllerexplorationmobile sensorssensor configuration

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Area of Science:

  • Robotics and Sensor Networks
  • Geophysical Exploration
  • Data Science

Background:

  • Mapping physical quantities like heat or electron density with mobile sensors presents a challenge.
  • Balancing exploration of new areas with refining data in known regions is difficult.

Purpose of the Study:

  • To develop a general framework for optimizing mobile sensor trajectories.
  • To address the conflicting goals of exploration and data refinement.

Main Methods:

  • Utilizing Gaussian process regression to predict physical quantity distributions.
  • Employing an epistemic utility controller for sensor trajectory decision-making.

Main Results:

  • The proposed framework enables value-laden decisions for sensor movement.
  • Demonstrated applicability and merit through a practical example.

Conclusions:

  • The framework offers a solution for efficient and effective spatial mapping using mobile sensors.
  • It provides a method to simultaneously explore and refine measurements in unknown environments.