{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:01:41Z","timestamp":1768280501016,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T00:00:00Z","timestamp":1707091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postgraduate Research Scholarships","award":["PGRS1912001"],"award-info":[{"award-number":["PGRS1912001"]}]},{"name":"Postgraduate Research Scholarships","award":["KSF-E-25"],"award-info":[{"award-number":["KSF-E-25"]}]},{"name":"Postgraduate Research Scholarships","award":["REF-19-01-03"],"award-info":[{"award-number":["REF-19-01-03"]}]},{"name":"Key Program Special Fund","award":["PGRS1912001"],"award-info":[{"award-number":["PGRS1912001"]}]},{"name":"Key Program Special Fund","award":["KSF-E-25"],"award-info":[{"award-number":["KSF-E-25"]}]},{"name":"Key Program Special Fund","award":["REF-19-01-03"],"award-info":[{"award-number":["REF-19-01-03"]}]},{"name":"Research Enhancement Fund of Xi\u2019an Jiaotong\u2013Liverpool University","award":["PGRS1912001"],"award-info":[{"award-number":["PGRS1912001"]}]},{"name":"Research Enhancement Fund of Xi\u2019an Jiaotong\u2013Liverpool University","award":["KSF-E-25"],"award-info":[{"award-number":["KSF-E-25"]}]},{"name":"Research Enhancement Fund of Xi\u2019an Jiaotong\u2013Liverpool University","award":["REF-19-01-03"],"award-info":[{"award-number":["REF-19-01-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence\/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)\u2014i.e., one of the state-of-the-art multi-building and multi-floor localization models\u2014and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of \u201cby a single building\u201d, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42\u00a0m.<\/jats:p>","DOI":"10.3390\/s24031026","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T05:22:44Z","timestamp":1707110564000},"page":"1026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8382-785X","authenticated-orcid":false,"given":"Zhe","family":"Tang","sequence":"first","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China"},{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4231-7455","authenticated-orcid":false,"given":"Sihao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China"},{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4123-2647","authenticated-orcid":false,"given":"Kyeong Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University (XJTLU), Suzhou 215123, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0212-2365","authenticated-orcid":false,"given":"Jeremy S.","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"ref_1","unstructured":"Leandro, R., Landau, H., Nitschke, M., Glocker, M., Seeger, S., Chen, X., Deking, A., BenTahar, M., Zhang, F., and Ferguson, K. 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