{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:38:21Z","timestamp":1773355101839,"version":"3.50.1"},"reference-count":0,"publisher":"Agora University of Oradea","issue":"2","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["INT J COMPUT COMMUN, Int. J. Comput. Commun. Control"],"abstract":"<jats:p>High-accuracy indoor positioning is essential for reliable wireless services and drone monitoring, yet the performance remains highly sensitive to access point (AP) failures. This paper proposes a fingerprinting-based localization framework utilizing Channel Impulse Response (CIR) features and a deep convolutional neural network (DCNN) to ensure robust positioning under both nominal and degraded operational conditions. Two scenarios are examined. In the first scenario, where all four APs operate normally, the influence of network depth and hyperparameter settings on localization accuracy is systematically evaluated. The network block count is progressively reduced and optimal hyperparameters are selected via the Hyperband algorithm, followed by parameter refinement through simulated annealing in a two-stage optimization strategy. Second, in single-AP failure conditions, a mitigation approach is employed wherein a single-input DCNN is retrained for each failure case, and its hyperparameters are independently optimized using Hyperband to compensate for reduced signal diversity. Experimental validation demonstrates that the proposed system achieves an average distance error (ADE) of 0.552 m under nominal conditions, outperforming existing methods. Under single-AP failure scenarios, the framework maintains strong positioning performance, with ADE ranging from 0.776 m to 1.019 m. These results confirm the effectiveness and resilience of the proposed DCNN architecture, highlighting its suitability for reliable indoor localization in realistic and fault-prone environments.<\/jats:p>","DOI":"10.15837\/ijccc.2026.2.7398","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:52Z","timestamp":1773315952000},"source":"Crossref","is-referenced-by-count":0,"title":["High-Accuracy Indoor Positioning Using Deep Neural Networks Combining with Optimal Hyperparameters and Fault-Tolerant Mechanism"],"prefix":"10.15837","volume":"21","author":[{"given":"Van-Phuc","family":"Hoang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thi-Kieu-Lan","family":"Ta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Van-Lan","family":"Dao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoang","family":"Thi Yen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"6528","published-online":{"date-parts":[[2026,3,12]]},"container-title":["INTERNATIONAL JOURNAL OF COMPUTERS  COMMUNICATIONS &amp; CONTROL"],"original-title":[],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:45:52Z","timestamp":1773315952000},"score":1,"resource":{"primary":{"URL":"https:\/\/univagora.ro\/jour\/index.php\/ijccc\/article\/view\/7398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.15837\/ijccc.2026.2.7398","relation":{},"ISSN":["1841-9844","1841-9836"],"issn-type":[{"value":"1841-9844","type":"electronic"},{"value":"1841-9836","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}