Measurement scheduling for soil moisture sensing: From physical models to optimal control
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...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/66142 https://orcid.org/0000-0002-8362-4761 |
| _version_ | 1826197689787744256 |
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| author | Shuman, David I. Nayyar, Ashutosh Mahajan, Aditya Goykhman, Yuriy Li, Ke Liu, Mingyan Teneketzis, Demosthenis Moghaddam, Mahta Entekhabi, Dara |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Shuman, David I. Nayyar, Ashutosh Mahajan, Aditya Goykhman, Yuriy Li, Ke Liu, Mingyan Teneketzis, Demosthenis Moghaddam, Mahta Entekhabi, Dara |
| author_sort | Shuman, David I. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:51:56Z |
| format | Article |
| id | mit-1721.1/66142 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:51:56Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/661422022-09-30T23:32:58Z Measurement scheduling for soil moisture sensing: From physical models to optimal control Shuman, David I. Nayyar, Ashutosh Mahajan, Aditya Goykhman, Yuriy Li, Ke Liu, Mingyan Teneketzis, Demosthenis Moghaddam, Mahta Entekhabi, Dara Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Entekhabi, Dara Entekhabi, Dara 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. United States. National Aeronautics and Space Administration. Advanced Information Systems Technology 2011-09-30T16:24:23Z 2011-09-30T16:24:23Z 2010-11 2010-04 Article http://purl.org/eprint/type/JournalArticle 0018-9219 INSPEC Accession Number: 11588465 http://hdl.handle.net/1721.1/66142 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 https://orcid.org/0000-0002-8362-4761 en_US http://dx.doi.org/10.1109/JPROC.2010.2052532 Proceedings of the IEEE Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Shuman, David I. Nayyar, Ashutosh Mahajan, Aditya Goykhman, Yuriy Li, Ke Liu, Mingyan Teneketzis, Demosthenis Moghaddam, Mahta Entekhabi, Dara Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title | Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title_full | Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title_fullStr | Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title_full_unstemmed | Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title_short | Measurement scheduling for soil moisture sensing: From physical models to optimal control |
| title_sort | measurement scheduling for soil moisture sensing from physical models to optimal control |
| url | http://hdl.handle.net/1721.1/66142 https://orcid.org/0000-0002-8362-4761 |
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