{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:12Z","timestamp":1760242872488,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,9,22]],"date-time":"2016-09-22T00:00:00Z","timestamp":1474502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61371135"],"award-info":[{"award-number":["No.61371135"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beihang University Innovation &amp; Practice Fund for Graduate","award":["YCSJ-02-2016-04"],"award-info":[{"award-number":["YCSJ-02-2016-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It\u2019s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.<\/jats:p>","DOI":"10.3390\/s16101547","type":"journal-article","created":{"date-parts":[[2016,9,22]],"date-time":"2016-09-22T09:59:55Z","timestamp":1474538395000},"page":"1547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks"],"prefix":"10.3390","volume":"16","author":[{"given":"Donghao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Jiangwen","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Junying","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.comnet.2014.03.027","article-title":"Energy efficiency in wireless sensor networks: A top-down survey","volume":"67","author":"Rault","year":"2014","journal-title":"Comput. 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