{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T03:28:52Z","timestamp":1770348532443,"version":"3.49.0"},"reference-count":14,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T00:00:00Z","timestamp":1726790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University","award":["RSPD2024R636"],"award-info":[{"award-number":["RSPD2024R636"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering\u2013learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring.<\/jats:p>","DOI":"10.3390\/s24186102","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Potential of Deep Learning in Underwater Wireless Sensor Networks and Noise Canceling for the Effective Monitoring of Aquatic Life"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-902X","authenticated-orcid":false,"given":"Walaa M.","family":"Elsayed","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Informatics, Damanhour University, Damanhour 22511, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8601-3184","authenticated-orcid":false,"given":"Maazen","family":"Alsabaan","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-4161","authenticated-orcid":false,"given":"Mohamed I.","family":"Ibrahem","sequence":"additional","affiliation":[{"name":"School of Computer and Cyber Sciences, Augusta University, Augusta, GA 30912, USA"},{"name":"Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11672, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4081-0553","authenticated-orcid":false,"given":"Engy","family":"El-Shafeiy","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City 32897, Monufia, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,20]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications","volume":"16","author":"Alsheikh","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"826","DOI":"10.11591\/ijece.v12i1.pp826-833","article-title":"An Internet of Things framework for real-time aquatic environment monitoring using an Arduino and sensors","volume":"12","author":"Islam","year":"2022","journal-title":"Int. J. Electr. Comput. Eng. (IJECE)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ACCESS.2020.3007502","article-title":"Underwater Networked Wireless Sensor Data Collection for Computational Intelligence Techniques: Issues, Challenges, and Approaches","volume":"8","author":"Gupta","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"614","article-title":"Fish survival prediction in an aquatic environment using random forest model","volume":"10","author":"Islam","year":"2021","journal-title":"IAES Int. J. Artif. Intell. (IJ-AI)"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rani, D., Sangwan, A., and Singh, T. (2021). Machine Learning Techniques for Underwater Wireless Sensor Networks: A Comprehensive Study. Energy-Efficient Underwater Wireless Communications, Copyright\u00a9 2021, IGI Global.","DOI":"10.4018\/978-1-7998-3640-7.ch013"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"799","DOI":"10.24425\/ijet.2022.143888","article-title":"Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks","volume":"68","author":"Prajapati","year":"2022","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.adhoc.2023.103139","article-title":"Machine learning approaches for underwater sensor network parameter prediction","volume":"144","author":"Uyan","year":"2023","journal-title":"Ad. Hoc. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/s23156973","article-title":"Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy","volume":"23","author":"Sathish","year":"2023","journal-title":"Sensors"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.comcom.2023.07.014","article-title":"Wadud A novel routing protocol for underwater wireless sensor networks based on shifted energy efficiency and priority","volume":"210","author":"Ismail","year":"2023","journal-title":"Comput. Commun."},{"key":"ref_10","first-page":"1","article-title":"Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique","volume":"23","author":"Alsabaan","year":"2023","journal-title":"J. Sens."},{"key":"ref_11","first-page":"1","article-title":"SIMO-Underwater Visible Light Communication (UVLC) system","volume":"232","author":"Ali","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ali, M.F., Jayakody, D.N.K., Beko, M., and Correia, S. (2023, January 20\u201322). Performance Evaluation of Vertical VLC Link in Mixed Water Mediums. Proceedings of the 2023 6th Conference on Cloud and Internet of Things (CIoT), Lisbon, Portugal.","DOI":"10.1109\/CIoT57267.2023.10084904"},{"key":"ref_13","first-page":"1","article-title":"Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration","volume":"1","author":"Zhang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fernandes, B., Gopi, B., Shariff, M.A., and Maniraj, S.P. (2024, January 8\u201310). Reliable and Efficient Routing for Water Quality Monitoring in Underwater WSN. 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