{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T21:08:38Z","timestamp":1761340118357,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,8]],"date-time":"2020-08-08T00:00:00Z","timestamp":1596844800000},"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":["U1805262, 61871131, 61701118, 61901117, and 61571128"],"award-info":[{"award-number":["U1805262, 61871131, 61701118, 61901117, and 61571128"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, China","award":["2018003"],"award-info":[{"award-number":["2018003"]}]},{"name":"Fujian provincial department of science and technology project","award":["2019J01267"],"award-info":[{"award-number":["2019J01267"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The weighted K-nearest neighbor (WKNN) algorithm is a commonly used fingerprint positioning, the difficulty of which lies in how to optimize the value of K to obtain the minimum positioning error. In this paper, we propose an adaptive residual weighted K-nearest neighbor (ARWKNN) fingerprint positioning algorithm based on visible light communication. Firstly, the target matches the fingerprints according to the received signal strength indication (RSSI) vector. Secondly, K is a dynamic value according to the matched RSSI residual. Simulation results show the ARWKNN algorithm presents a reduced average positioning error when compared with random forest (81.82%), extreme learning machine (83.93%), artificial neural network (86.06%), grid-independent least square (60.15%), self-adaptive WKNN (43.84%), WKNN (47.81%), and KNN (73.36%). These results were obtained when the signal-to-noise ratio was set to 20 dB, and Manhattan distance was used in a two-dimensional (2-D) space. The ARWKNN algorithm based on Clark distance and minimum maximum distance metrics produces the minimum average positioning error in 2-D and 3-D, respectively. Compared with self-adaptive WKNN (SAWKNN), WKNN and KNN algorithms, the ARWKNN algorithm achieves a significant reduction in the average positioning error while maintaining similar algorithm complexity.<\/jats:p>","DOI":"10.3390\/s20164432","type":"journal-article","created":{"date-parts":[[2020,8,10]],"date-time":"2020-08-10T05:07:23Z","timestamp":1597036043000},"page":"4432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8735-5813","authenticated-orcid":false,"given":"Shiwu","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China"},{"name":"Concord University College, Fujian Normal University, Fuzhou 350117, China"}]},{"given":"Chih-Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan"},{"name":"Information and Engineering College, Jimei University, Fujian 361021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5704-2111","authenticated-orcid":false,"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China"}]},{"given":"Xufang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4180-8295","authenticated-orcid":false,"given":"Fen","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/COMST.2018.2867935","article-title":"Indoor positioning technologies without offline fingerprinting map: A survey","volume":"21","author":"Jang","year":"2019","journal-title":"IEEE Commun. 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