{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:21:27Z","timestamp":1776129687875,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Underwater Wireless Sensor Networks (UWSNs) play a critical role in collecting data for various marine applications, but their deployment and maintenance in harsh underwater environments pose significant challenges. Building upon our prior conceptual work, this article presents a practical implementation of an AI\u2010driven fault detection system for UWSNs, a key component of a future predictive maintenance framework incorporating Named Data Networking (NDN). Using the \u201cUnderwater Sensor Dataset,\u201d we implemented a feed\u2010forward neural network to classify sensor readings as healthy or faulty. The model achieved high accuracy (99.9%), precision (100%), recall (99.1%), and F1\u2010score (99.6%) on the test set, demonstrating its effectiveness in fault detection. Our findings highlight the feasibility and potential of AI\u2010based predictive maintenance in UWSNs, even with a simpler neural network model. The framework promises reduced downtime, minimized maintenance costs, and an extended lifespan for UWSNs.<\/jats:p>","DOI":"10.1002\/itl2.630","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:14:18Z","timestamp":1735258458000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Maintenance for\n                    <scp>UWSNs<\/scp>\n                    : A Practical Implementation of Fault Detection Using\n                    <scp>NDN<\/scp>\n                    and Deep Learning"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2192-5048","authenticated-orcid":false,"given":"Abdelmadjid","family":"Benarfa","sequence":"first","affiliation":[{"name":"Laboratoire d'Informatique et de Math\u00e9matiques Universit\u00e9 de Laghouat  Laghouat Algeria"}]},{"given":"Sofiane","family":"Dahmane","sequence":"additional","affiliation":[{"name":"Computer Science Department Ecole Normale Sup\u00e9rieure  Laghouat Algeria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3267-5702","authenticated-orcid":false,"given":"Bouziane","family":"Brik","sequence":"additional","affiliation":[{"name":"Computer Science Department College of Computing and Informatics, Sharjah University  Sharjah UAE"}]},{"given":"Mohamed Bachir","family":"Yagoubi","sequence":"additional","affiliation":[{"name":"Laboratoire d'Informatique et de Math\u00e9matiques Universit\u00e9 de Laghouat  Laghouat Algeria"}]}],"member":"311","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"e_1_2_1_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3218766"},{"key":"e_1_2_1_9_3_1","first-page":"141","article-title":"A Review on Machine Learning for Predictive Maintenance","volume":"42","author":"Wang P.","year":"2017","journal-title":"Journal of Manufacturing Systems"},{"key":"e_1_2_1_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCNS62192.2024.10776363"},{"issue":"1","key":"e_1_2_1_9_5_1","first-page":"2","article-title":"Underwater Wireless Sensor Networks: A Comprehensive Survey","volume":"15","author":"Li J.","year":"2015","journal-title":"Sensors"},{"key":"e_1_2_1_9_6_1","unstructured":"\u00d6.Teke \u201cDEPCI\u02d9$$ \\dot{\\mathrm{I}} $$T. Predictive Maintenance in Maritime Logistics: A Machine Learning Approach \u201d DELOK'23."},{"key":"e_1_2_1_9_7_1","doi-asserted-by":"crossref","unstructured":"J.Zhong Z.Yang andS.Wong \u201cMachine Condition Monitoring and Fault Diagnosis Based on Support Vector Machine \u201d in 2010 IEEE International Conference on Industrial Engineering and Engineering Management Macao China(2010) 2228\u20132233.","DOI":"10.1109\/IEEM.2010.5674594"},{"key":"e_1_2_1_9_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11101563"},{"key":"e_1_2_1_9_9_1","volume-title":"Introduction to Statistical Quality Control","author":"Montgomery D. 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