In-Situ Soil Moisture Sensing: From Physical Models to Optimal Control to System Deployment

Prof. Mingyan Liu (
EECS, University of Michigan

2:30pm, June 3, 2014.

Mingyan Liu received her B.Sc. degree in electrical engineering in 1995 from the Nanjing University of Aero. and Astro., Nanjing, China, M.Sc. degree in systems engineering and Ph.D. Degree in electrical engineering from the University of Maryland, College Park, in 1997 and 2000, respectively. She has since been with the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, where she is currently a Professor. Her research interests are in optimal resource allocation, performance modeling and analysis, and energy efficient design of wireless, mobile ad hoc, and sensor networks. She is the recipient of the 2002 NSF CAREER Award, the University of Michigan Elizabeth C. Crosby Research Award in 2003, and the 2010 EECS Department Outstanding Achievement Award. She also received a Best Paper Award at the International Conference on Information Processing in Sensor Networks (IPSN) in 2012. She serves/has served on the editorial board of IEEE/ACM Trans. Networking, IEEE Trans. Mobile Computing, and ACM Trans. Sensor Networks. She is a member of the ACM and Fellow of the IEEE.

In this talk I will describe the monitoring of soil moisture evolution using a wireless network of in-situ sensors. Soil moisture measurement has many applications in hydrology and is one of the most important indicators in agricultural drought monitoring. Traditionally soil moisture data have been collected solely through remote sensing, i.e., from satellite radars and radiometers. These measurements allow global mapping but have large footprints. In-situ soil moisture sensors can capture variability at much finer spatial and temporal scales but a large deployment is costly and impractical. The objective of this study is to improve the scalability of in-situ soil moisture sensing through optimal measurement scheduling and judicial sensor placement so as to allow sparse measurements (both spatially and temporally) to meet monitoring needs, which include using in-situ measurements to validate remote sensing.

Specifically, I will present the conceptualization of the optimal measurement scheduling framework; this is formulated as a partially observable Markov decision problem (POMDP) by using statistics of soil moisture evolution from a physical model. A second measurement scheduling framework is then introduced based on sparse sampling and compressive sensing techniques. I then describe our instrumentation and system integration effort and a recent field deployment of this wireless monitoring system on farms in Oklahoma and California, along with experience and lessons learned in building practical unattended wireless sensor networks.

This talk is based on joint work with Profs. M. Moghaddam, D. Teneketzis, D. Entekhabi, and our team of students.