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Exploration vs. Data Refinement via Multiple Mobile Sensors

Electrical and Computer Engineering Department and Information Dynamics Laboratory, Utah State University, 4120 Old Main Hill, Logan, UT 84322-4120, USA
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Entropy 2019, 21(6), 568; https://doi.org/10.3390/e21060568
Received: 22 April 2019 / Revised: 2 June 2019 / Accepted: 4 June 2019 / Published: 5 June 2019
(This article belongs to the Section Signal and Data Analysis)
PDF [1162 KB, uploaded 5 June 2019]

Abstract

We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.
Keywords: sensor configuration; adaptive sampling; exploration; data refinement; mobile sensors; epistemic utility controller; Gaussian process regression (GPR); decision under conflict sensor configuration; adaptive sampling; exploration; data refinement; mobile sensors; epistemic utility controller; Gaussian process regression (GPR); decision under conflict
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Shekaramiz, M.; Moon, T.K.; Gunther, J.H. Exploration vs. Data Refinement via Multiple Mobile Sensors. Entropy 2019, 21, 568.

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