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Measurement scheduling for soil moisture sensing: From physical models to optimal control

Author(s)
Shuman, David I.; Nayyar, Ashutosh; Mahajan, Aditya; Goykhman, Yuriy; Li, Ke; Liu, Mingyan; Teneketzis, Demosthenis; Moghaddam, Mahta; Entekhabi, Dara; ... Show more Show less
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Abstract
In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.
Date issued
2010-11
URI
http://hdl.handle.net/1721.1/66142
Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Journal
Proceedings of the IEEE
Publisher
Institute of Electrical and Electronics Engineers
Citation
Shuman, D.I. et al. “Measurement Scheduling for Soil Moisture Sensing: From Physical Models to Optimal Control.” Proceedings of the IEEE 98.11 (2010): 1918-1933. Copyright © 2010, IEEE
Version: Final published version
Other identifiers
INSPEC Accession Number: 11588465
ISSN
0018-9219

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