{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:26:12Z","timestamp":1767338772855,"version":"3.41.2"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2023,9,27]]},"abstract":"<jats:p>We propose a novel Visible Light Positioning (VLP) method, called Iris, that leverages light spectral information (LSI) to localize individuals in a completely passive manner. This means that the user does not need to carry any device, and the existing lighting infrastructure remains unchanged. Our method uses a background subtraction approach to accurately detect changes in ambient LSI caused by human movement. Furthermore, we design a Convolutional Neural Network (CNN) capable of learning and predicting user locations from the LSI change data.<\/jats:p>\n          <jats:p>To validate our approach, we implemented a prototype of Iris using a commercial-off-the-shelf light spectral sensor and conducted experiments in two typical real-world indoor environments: a 25 m2 one-bedroom apartment and a 13.3m \u00d7 8.4m office space. Our results demonstrate that Iris performs effectively in both artificial lighting at night and in highly dynamic natural lighting conditions during the day. Moreover, Iris outperforms the state-of-the-art passive VLP techniques significantly in terms of localization accuracy and the required density of light sensors.<\/jats:p>\n          <jats:p>To reduce the overhead associated with multi-channel spectral sensing, we develop and validate an algorithm that can minimize the required number of spectral channels for a given environment. Finally, we propose a conditional Generative Adversarial Network (cGAN) that can artificially generate LSI and reduce data collection effort by 50% without sacrificing localization accuracy.<\/jats:p>","DOI":"10.1145\/3610913","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T15:45:03Z","timestamp":1695829503000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Iris"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0336-4144","authenticated-orcid":false,"given":"Jiawei","family":"Hu","sequence":"first","affiliation":[{"name":"University of New South Wales and CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1466-4006","authenticated-orcid":false,"given":"Yanxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of New South Wales and CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6047-4158","authenticated-orcid":false,"given":"Hong","family":"Jia","sequence":"additional","affiliation":[{"name":"University of Cambridge, United of Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4076-1811","authenticated-orcid":false,"given":"Wen","family":"Hu","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3417-8590","authenticated-orcid":false,"given":"Mahbub","family":"Hassan","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9082-3243","authenticated-orcid":false,"given":"Brano","family":"Kusy","sequence":"additional","affiliation":[{"name":"CSIRO, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7144-8254","authenticated-orcid":false,"given":"Ashraf","family":"Uddin","sequence":"additional","affiliation":[{"name":"University of New South Wales, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2063-4364","authenticated-orcid":false,"given":"Moustafa","family":"Youssef","sequence":"additional","affiliation":[{"name":"AUC and Alexandria University, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2014.7275528"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2019.8767421"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"e_1_2_1_4_1","unstructured":"ams OSRAM. 2022. 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