{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:52:47Z","timestamp":1777445567444,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2014,9,9]],"date-time":"2014-09-09T00:00:00Z","timestamp":1410220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We address the problem of data acquisition in large distributed wireless sensor networks (WSNs). We propose a method for data acquisition using the hierarchical routing method and compressive sensing for WSNs. Only a few samples are needed to recover the original signal with high probability since sparse representation technology is exploited to capture the similarities and differences of the original signal. To collect samples effectively in WSNs, a framework for the use of the hierarchical routing method and compressive sensing is proposed, using a randomized rotation of cluster-heads to evenly distribute the energy load among the sensors in the network. Furthermore, L1-minimization and Bayesian compressed sensing are used to approximate the recovery of the original signal from the smaller number of samples with a lower signal reconstruction error. We also give an extensive validation regarding coherence, compression rate, and lifetime, based on an analysis of the theory and experiments in the environment with real world signals. The results show that our solution is effective in a large distributed network, especially for energy constrained WSNs.<\/jats:p>","DOI":"10.3390\/s140916766","type":"journal-article","created":{"date-parts":[[2014,9,9]],"date-time":"2014-09-09T09:51:28Z","timestamp":1410256288000},"page":"16766-16784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["WSNs Data Acquisition by Combining Hierarchical Routing Method and Compressive Sensing"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhiqiang","family":"Zou","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,  Nanjing 210003, China"},{"name":"Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA"}]},{"given":"Cunchen","family":"Hu","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Fei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Hao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}]},{"given":"Shu","family":"Shen","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing 210003, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,  Nanjing 210003, China"},{"name":"Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA"}]}],"member":"1968","published-online":{"date-parts":[[2014,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1109\/JSAC.2011.110915","article-title":"Random access compressed sensing for energy-efficient underwater sensor networks","volume":"29","author":"Fazel","year":"2011","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_2","unstructured":"Wang, R., and Xiao, F. Advances in Wireless Sensor Networks, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.adhoc.2003.09.010","article-title":"A survey on routing protocols for wireless sensor networks","volume":"3","author":"Akkaya","year":"2005","journal-title":"Ad. Hoc. Netw."},{"key":"ref_4","unstructured":"Heinzelman, W.R., Chandrakasan, A.P., and Balakrishnan, H. (2000, January 4\u20137). Energy efficient communication protocol for wireless sensor networks. Hawaii, HI, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2007.914731","article-title":"An introduction to compressive sampling","volume":"25","author":"Wakin","year":"2008","journal-title":"IEEE Signal Proc. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_7","first-page":"608","article-title":"Robust wireless sensor networks with compressed sensing theory","volume":"293","author":"Balouchestani","year":"2012","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13034","DOI":"10.3390\/s121013034","article-title":"Expanding window compressed sensing for non-uniform compressible signals","volume":"12","author":"Liu","year":"2012","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Luo, J., Xiang, L., and Rosenberg, C. (2010, January 23\u201327). Does compressed sensing improve the throughput of wireless sensor networks. Cape Town, South Africa.","DOI":"10.1109\/ICC.2010.5502565"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xiang, L., Luo, J., and Vasilakos, A.V. (2011, January 27\u201330). Compressed data aggregation for energy efficient wireless sensor networks. Salt Lake City, UT, USA.","DOI":"10.1109\/SAHCN.2011.5984932"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xiang, L., Luo, J., Deng, C., Vasilakos, A., and Lin, W. (2012, January 18\u201321). DECA: Recovering fields of physical quantities from incomplete sensory data. Seoul, Korea.","DOI":"10.1109\/SECON.2012.6275775"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1109\/TNET.2012.2229716","article-title":"Compressed data aggregation: Energy-Efficient and high-fidelity data collection","volume":"12","author":"Xiang","year":"2013","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3447","DOI":"10.1109\/TWC.2012.081612.110612","article-title":"Sensing compression and recovery for WSNS sparse signal modeling and monitoring framework","volume":"11","author":"Quer","year":"2012","journal-title":"IEEE Trans. Wirel. Commnun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neucom.2013.01.032","article-title":"A dynamic neural network approach for solving nonlinear inequalities defined on a graph and its application to distributed, routing-free, range-free localization of WSNs","volume":"117","author":"Li","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Quer, G., Masiero, R., Munaretto, D., Rossi, M., Widmer, J., and Zorzi, M. (2009, January 8\u201313). On the interplay between routing and signal representation for Compressive Sensing in wireless sensor networks. San Diego, CA, USA.","DOI":"10.1109\/ITA.2009.5044947"},{"key":"ref_16","unstructured":"Smith, L.I. A Tutorial on Principal Components Analysis. Available online: www.ce.yildiz.edu.tr\/personal\/songul\/file\/1097\/principal_components.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"On lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Philos. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1109\/TPDS.2012.216","article-title":"Does wireless sensor network scale? A measurement study on GreenOrbs","volume":"24","author":"Liu","year":"2013","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_19","unstructured":"Masiero, R., Quer, G., Munaretto, D., Rossi, M., Widmer, J., and Zorzi, M. (December, January 30). Data acquisition through joint compressive sensing and principal component analysis. Honolulu, HI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luo, C., Wu, F., Sun, J, and Chen, C.W. (2009, January 20\u201325). Compressive data gathering for large-scale wireless sensor networks. Beijing, China.","DOI":"10.1145\/1614320.1614337"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Masiero, R., Quer, G., Rossi, M., and Zorzi, M. (2009, January 12\u201314). A bayesian analysis of compressive sensing data recovery in wireless sensor networks. St. Petersburg, FL, USA.","DOI":"10.1109\/ICUMT.2009.5345599"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2346","DOI":"10.1109\/TSP.2007.914345","article-title":"Bayesian compressive sensing","volume":"56","author":"Ji","year":"2008","journal-title":"IEEE Trans. Signal Proc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, B., Li, J.Z., and Li, Y.S. (2009, January 19\u201325). Distributed data aggregation scheduling in wireless sensor networks. Rio de Janeiro, Brazil.","DOI":"10.1109\/INFCOM.2009.5062140"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1109\/TPDS.2010.80","article-title":"A Delay efficient algorithm for data aggregation in multi-hop wireless sensor networks","volume":"22","author":"Xu","year":"2011","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1109\/TWC.2002.804190","article-title":"An application specific protocol architecture for wireless microsensor networks","volume":"1","author":"Heinzelman","year":"2002","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_26","unstructured":"Lindsey, S., and Raghavendra, C.S. (2002, January 9\u201316). PEGASIS: Power-Efficient gathering in sensor information systems. Big Sky, MT, USA."},{"key":"ref_27","unstructured":"Candes, E., and Romberg, J. L1-Magic: Recovery Of Sparse Signals Via Convex Programming. Available online: www.acm.caltech.edu\/l1magic\/downloads\/l1magic.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/9\/16766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:15:43Z","timestamp":1760217343000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/9\/16766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,9]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2014,9]]}},"alternative-id":["s140916766"],"URL":"https:\/\/doi.org\/10.3390\/s140916766","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,9,9]]}}}