{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T12:17:08Z","timestamp":1763468228170,"version":"3.41.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2014,7,29]],"date-time":"2014-07-29T00:00:00Z","timestamp":1406592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61373146"],"award-info":[{"award-number":["61373146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["NNX12AO54G","NNX09AE91G","NNX06AD47G"],"award-info":[{"award-number":["NNX12AO54G","NNX09AE91G","NNX06AD47G"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007270","name":"University of Michigan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2015,3,2]]},"abstract":"<jats:p>We consider the problem of monitoring soil moisture evolution using a wireless network of in-situ underground sensors. To reduce cost and prolong lifetime, it is highly desirable to rely on fewer measurements and estimate with higher accuracy the original signal (the temporal evolution of soil moisture). In this article, we explore the use of results from the theory of sparse sampling, including Compressive Sensing (CS) and Matrix Completion (MC), in this application context. We first consider the problem of reconstructing the soil moisture process at a single location using CS. Our physical constraint leads to very sparse measurement matrices, which makes finding a suitable representation basis very challenging: it needs to make the underlying signal sufficiently sparse while at the same time being sufficiently incoherent with the measurement matrix, two common preconditions for CS techniques to work well. We construct a representation basis by exploiting unique features of soil moisture evolution and show that this basis attains a very good tradeoff between its ability to sparsify the signal and its incoherence with measurement matrices that are consistent with our physical constraints. We next consider the problem of jointly reconstructing soil moisture processes at multiple locations, assuming sparse measurements can be taken at each location. We show that the spatial soil moisture process enjoys a low-rank property, a priority for MC. Accordingly, we introduce a spatiotemporal measurement matrix and apply the MC framework to reconstruct the soil moisture field. Extensive numerical evaluation is performed on both real, high-resolution soil moisture data and simulated data and through comparison with a closed-loop scheduling approach. Our results demonstrate that, for a single location, a uniform measurement scheduling followed by CS recovery results in a very nice tradeoff between estimation accuracy, sampling rate, flexibility, and feasibility in implementation. When multiple locations are available, our results show that joint reconstruction using MC in general produces better estimation accuracy than using a single location alone, but it requires the use of independent and random measurement schedules across locations. We also show that these sparse sampling techniques can be augmented so as to be robust against sporadic data outliers\/corruption caused by, for example, intermittent sensor faults.<\/jats:p>","DOI":"10.1145\/2629439","type":"journal-article","created":{"date-parts":[[2014,8,1]],"date-time":"2014-08-01T20:13:24Z","timestamp":1406924004000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["In-situ Soil Moisture Sensing"],"prefix":"10.1145","volume":"11","author":[{"given":"Xiaopei","family":"Wu","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China, University of Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingsi","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,7,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2002.1024422"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02275352"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(72)90099-4"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1137\/0327042"},{"key":"e_1_2_1_5_1","first-page":"12","article-title":"Distributed compressed sensing","volume":"52","author":"Baron D.","year":"2006","unstructured":"D. Baron , M. B. Wakin , M. F. Duarte , S. Sarvotham , and R. G. Baraniuk . 2006 . Distributed compressed sensing . IEEE Transactions on Information Theory 52 , 12 (December 2006), 5406--5425. D. Baron, M. B. Wakin, M. F. Duarte, S. Sarvotham, and R. G. Baraniuk. 2006. Distributed compressed sensing. IEEE Transactions on Information Theory 52, 12 (December 2006), 5406--5425.","journal-title":"IEEE Transactions on Information Theory"},{"key":"e_1_2_1_6_1","unstructured":"Stephen Becker. 2012. Matrix Completion Solvers. (2012). http:\/\/www.ugcs.caltech.edu\/&sim;srbecker\/wiki\/Category:Matrix_Completion_Solvers.  Stephen Becker. 2012. Matrix Completion Solvers. (2012). http:\/\/www.ugcs.caltech.edu\/&sim;srbecker\/wiki\/Category:Matrix_Completion_Solvers."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1137\/080738970"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.885507"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Congress of Mathematicians. 265--272","author":"Cand\u00e9s Emmanuel J.","year":"2006","unstructured":"Emmanuel J. Cand\u00e9s . 2006 . Compressive sampling . In Proceedings of the International Congress of Mathematicians. 265--272 . Emmanuel J. Cand\u00e9s. 2006. Compressive sampling. In Proceedings of the International Congress of Mathematicians. 265--272."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-009-9045-5"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.862083"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.858979"},{"volume-title":"Parallelizing the Distributed Hydrologic Model MOBIDIC","author":"Castillo Aldrich","key":"e_1_2_1_13_1","unstructured":"Aldrich Castillo . 2010. Parallelizing the Distributed Hydrologic Model MOBIDIC . Technical Report. Environmental Engineering , Massachusetts Institute of Technology . Aldrich Castillo. 2010. Parallelizing the Distributed Hydrologic Model MOBIDIC. Technical Report. Environmental Engineering, Massachusetts Institute of Technology."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/090761793"},{"volume-title":"Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings (ISIT). 2313--2317","author":"Chen Yudong","key":"e_1_2_1_15_1","unstructured":"Yudong Chen , A. Jalali , S. Sanghavi , and C. Caramanis . 2011a. Low-rank matrix recovery from errors and erasures . In Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings (ISIT). 2313--2317 . Yudong Chen, A. Jalali, S. Sanghavi, and C. Caramanis. 2011a. Low-rank matrix recovery from errors and erasures. In Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings (ISIT). 2313--2317."},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 28th International Conference on Machine Learning. 873--880","author":"Chen Yudong","year":"2011","unstructured":"Yudong Chen , Huan Xu , Constantine Caramanis , and Sujay Sanghavi . 2011 b. Robust matrix completion with corrupted columns . In Proceedings of the 28th International Conference on Machine Learning. 873--880 . Yudong Chen, Huan Xu, Constantine Caramanis, and Sujay Sanghavi. 2011b. Robust matrix completion with corrupted columns. In Proceedings of the 28th International Conference on Machine Learning. 873--880."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2011.2169424"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2012.121412.120148"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2144977"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/1316689.1316741"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"e_1_2_1_22_1","unstructured":"D. L. Donoho. 2004. For Most Large Underdetermined Systems of Equations the Minimal l1-norm Near-Solution Approximates the Sparsest Near-Solution. Technology Report.  D. L. Donoho. 2004. For Most Large Underdetermined Systems of Equations the Minimal l1-norm Near-Solution Approximates the Sparsest Near-Solution. Technology Report."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2005.860430"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207170110089752"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-011-9084-6"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.558475"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2009.2025528"},{"volume-title":"Proceedings of the Neural Information Processing Systems Conference (NIPS). 937--945","author":"Jain P.","key":"e_1_2_1_28_1","unstructured":"P. Jain , R. Meka , and I. Dhillont . 2010. Guaranteed rank minimization via singular value projection . In Proceedings of the Neural Information Processing Systems Conference (NIPS). 937--945 . P. Jain, R. Meka, and I. Dhillont. 2010. Guaranteed rank minimization via singular value projection. In Proceedings of the Neural Information Processing Systems Conference (NIPS). 937--945."},{"volume-title":"Proceedings of the 33rd IEEE International Conference on Computer Communications (INFOCOM\u201914)","author":"Ji X.","key":"e_1_2_1_29_1","unstructured":"X. Ji , Y. He , J. Wang , W. Dong , X. Wu , and Y. Liu . 2014. Walking down the STAIRS: Efficient collision resolution for wireless sensor networks . In Proceedings of the 33rd IEEE International Conference on Computer Communications (INFOCOM\u201914) . X. Ji, Y. He, J. Wang, W. Dong, X. Wu, and Y. Liu. 2014. Walking down the STAIRS: Efficient collision resolution for wireless sensor networks. In Proceedings of the 33rd IEEE International Conference on Computer Communications (INFOCOM\u201914)."},{"key":"e_1_2_1_30_1","volume-title":"Optspace: A gradient descent algorithm on the Grassman manifold for matrix completion.","author":"Keshavan R. H.","year":"2009","unstructured":"R. H. Keshavan and S. Oh . 2009 . Optspace: A gradient descent algorithm on the Grassman manifold for matrix completion. Retrieved http:\/\/arxiv.org\/abs\/0910.5260v2\/. R. H. Keshavan and S. Oh. 2009. Optspace: A gradient descent algorithm on the Grassman manifold for matrix completion. Retrieved http:\/\/arxiv.org\/abs\/0910.5260v2\/."},{"key":"e_1_2_1_31_1","article-title":"Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies","author":"Krause Andreas","year":"2008","unstructured":"Andreas Krause , Ajit Singh , and Carlos Guestrin . 2008 . Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies . Journal of Machine Learning Research 9 ( June 2008), 235--284. Andreas Krause, Ajit Singh, and Carlos Guestrin. 2008. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research 9 (June 2008), 235--284.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2002.1003062"},{"key":"e_1_2_1_33_1","unstructured":"l1-magic. 2005. l1-magic: Recovery of Sparse Signals Via Convex Programming. Retrieved from www.acm.caltech.edu\/l1magic\/downloads\/l1magic.pdf.  l1-magic. 2005. l1-magic: Recovery of Sparse Signals Via Convex Programming. Retrieved from www.acm.caltech.edu\/l1magic\/downloads\/l1magic.pdf."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015467.1015492"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2007.05.004"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1614320.1614337"},{"key":"e_1_2_1_37_1","unstructured":"Angshul Majumdar. 2010. Matlab Source Code. Retrieved from http:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/26395-matrix-completion-via-thresholding.  Angshul Majumdar. 2010. Matlab Source Code. Retrieved from http:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/26395-matrix-completion-via-thresholding."},{"key":"e_1_2_1_38_1","first-page":"4","article-title":"A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations. Selected Topics in Applied Earth Observations and Remote Sensing","volume":"3","author":"Moghaddam M.","year":"2010","unstructured":"M. Moghaddam , D. Entekhabi , Y. Goykhman , Ke Li , Mingyan Liu , A. Mahajan , A. Nayyar , D. Shuman , and D. Teneketzis . 2010 a. A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations. Selected Topics in Applied Earth Observations and Remote Sensing , IEEE Journal of 3 , 4 (Dec 2010), 522--535. M. Moghaddam, D. Entekhabi, Y. Goykhman, Ke Li, Mingyan Liu, A. Mahajan, A. Nayyar, D. Shuman, and D. Teneketzis. 2010a. A wireless soil moisture smart sensor web using physics-based optimal control: Concept and initial demonstrations. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 3, 4 (Dec 2010), 522--535.","journal-title":"IEEE Journal of"},{"key":"e_1_2_1_39_1","unstructured":"M. Moghaddam X. Wu M. Burgin A. Castillo D. Entekhabi Y. Goykhman K. Li M. Liu M. Liu A. Nayyar A. Silva Q. Wang and D. Teneketzis. 2010b. Soil moisture sensing controller and optimal estimator (SoilSCAPE): First deployment of the wireless sensor network and latest progress on soil moisture satellite retrieval validation strategies. In Earth Science Technology Forum (ESTF\u201910).  M. Moghaddam X. Wu M. Burgin A. Castillo D. Entekhabi Y. Goykhman K. Li M. Liu M. Liu A. Nayyar A. Silva Q. Wang and D. Teneketzis. 2010b. Soil moisture sensing controller and optimal estimator (SoilSCAPE): First deployment of the wireless sensor network and latest progress on soil moisture satellite retrieval validation strategies. In Earth Science Technology Forum (ESTF\u201910)."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.2007606"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.28.1.1"},{"key":"e_1_2_1_42_1","unstructured":"NASASP. 2006. NASA Strategic Plan. Retrieved from http:\/\/www.nasa.gov\/.  NASASP. 2006. NASA Strategic Plan. Retrieved from http:\/\/www.nasa.gov\/."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2042412"},{"volume-title":"Proceedings of the Information Theory and Applications Workshop (ITA\u201909)","author":"Quer G.","key":"e_1_2_1_44_1","unstructured":"G. Quer , R. Masiero , D. Munaretto , M. Rossi , J. Widmer , and M. Zorzi . 2009. On the interplay between routing and signal representation for compressive sensing in wireless sensor networks . In Proceedings of the Information Theory and Applications Workshop (ITA\u201909) . 206--215. G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer, and M. Zorzi. 2009. On the interplay between routing and signal representation for compressive sensing in wireless sensor networks. In Proceedings of the Information Theory and Applications Workshop (ITA\u201909). 206--215."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644038.1644058"},{"key":"e_1_2_1_46_1","first-page":"11","article-title":"Measurement scheduling for soil moisture sensing: From physical models to optimal control","volume":"98","author":"Shuman David","year":"2010","unstructured":"David Shuman , Ashutosh Nayyar , Aditya Mahajan , Yuriy Goykhman , Ke Li , Mingyan Liu , Demosthenis Teneketzis , Mahta Moghaddam , and Dara Entekhab . 2010 . Measurement scheduling for soil moisture sensing: From physical models to optimal control . Proceedings of the IEEE Special Issue on Sensor Networks and Applications 98 , 11 (November 2010), 1918--1933. David Shuman, Ashutosh Nayyar, Aditya Mahajan, Yuriy Goykhman, Ke Li, Mingyan Liu, Demosthenis Teneketzis, Mahta Moghaddam, and Dara Entekhab. 2010. Measurement scheduling for soil moisture sensing: From physical models to optimal control. Proceedings of the IEEE Special Issue on Sensor Networks and Applications 98, 11 (November 2010), 1918--1933.","journal-title":"Proceedings of the IEEE Special Issue on Sensor Networks and Applications"},{"key":"e_1_2_1_47_1","unstructured":"SL0. 2008. SL0. Retrieved from http:\/\/ee.sharif.ir\/&sim;SLzero\/.  SL0. 2008. SL0. Retrieved from http:\/\/ee.sharif.ir\/&sim;SLzero\/."},{"key":"e_1_2_1_48_1","unstructured":"Sparse Lab. 2007. Sparse Lab. Retrieved from http:\/\/sparselab.stanford.edu\/.  Sparse Lab. 2007. Sparse Lab. Retrieved from http:\/\/sparselab.stanford.edu\/."},{"volume-title":"Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201907)","author":"Tian Z.","key":"e_1_2_1_49_1","unstructured":"Z. Tian and G. Giannakis . 2007. Compressed sensing for wide band cognitive radios . In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201907) . 1357--1360. Z. Tian and G. Giannakis. 2007. Compressed sensing for wide band cognitive radios. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP\u201907). 1357--1360."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2007.909108"},{"volume-title":"Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM\u201911)","author":"Wang J.","key":"e_1_2_1_51_1","unstructured":"J. Wang , Y. Liu , M. Li , M. Rossi , W. Dong , and Y. He . 2011. Qof: Towards comprehensive path quality measurement in wireless sensor networks . In Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM\u201911) . 775--783. J. Wang, Y. Liu, M. Li, M. Rossi, W. Dong, and Y. He. 2011. Qof: Towards comprehensive path quality measurement in wireless sensor networks. In Proceedings of the 30th IEEE International Conference on Computer Communications (INFOCOM\u201911). 775--783."},{"key":"e_1_2_1_52_1","unstructured":"Andrew E. Waters Aswin C. Sankaranarayanan and Richard Baraniuk. 2011. SpaRCS: Recovering low-rank and sparse matrices from compressive measurements. In Advances in Neural Information Processing Systems. 1089--1097.  Andrew E. Waters Aswin C. Sankaranarayanan and Richard Baraniuk. 2011. SpaRCS: Recovering low-rank and sparse matrices from compressive measurements. In Advances in Neural Information Processing Systems. 1089--1097."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2240116.2240122"},{"volume-title":"Proceedings of International Conference on Wireless Communications, Networking and Mobile Computation. 1--6.","author":"Wu X.","key":"e_1_2_1_54_1","unstructured":"X. Wu , Y. Wu , M. Liu , and L. Zheng . 2011. In-situ soil moisture sensing: efficient random sensor placement and field estimation using compressive sensing . In Proceedings of International Conference on Wireless Communications, Networking and Mobile Computation. 1--6. X. Wu, Y. Wu, M. Liu, and L. Zheng. 2011. In-situ soil moisture sensing: efficient random sensor placement and field estimation using compressive sensing. In Proceedings of International Conference on Wireless Communications, Networking and Mobile Computation. 1--6."}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2629439","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2629439","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:01:17Z","timestamp":1750230077000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2629439"}},"subtitle":["Measurement Scheduling and Estimation Using Sparse Sampling"],"short-title":[],"issued":{"date-parts":[[2014,7,29]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2015,3,2]]}},"alternative-id":["10.1145\/2629439"],"URL":"https:\/\/doi.org\/10.1145\/2629439","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"type":"print","value":"1550-4859"},{"type":"electronic","value":"1550-4867"}],"subject":[],"published":{"date-parts":[[2014,7,29]]},"assertion":[{"value":"2013-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2014-07-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}