{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T04:50:38Z","timestamp":1774327838602,"version":"3.50.1"},"reference-count":41,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T00:00:00Z","timestamp":1737244800000},"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":["Int J Communication"],"published-print":{"date-parts":[[2025,3,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Several changes have been implemented over the years to provide better resource management and service delivery for artificial wireless sensor networks (WSNs) that rely on the Internet of Things (IoT). Here, 5G networks offer high data rates with ultra\u2010low latency and robust reliability, which is essential for managing the substantial data volumes generated by IoT devices in 5G WSNs. IoT needs an optimal communication network to transmit data among different devices. The whole network is categorized as heterogeneous clusters in clustering. The cluster head (CH) selection achieves proficient data communication to the sink node through the chosen CH. In this manuscript, an energy\u2010efficient communication using auto\u2010associative polynomial convolutional neural network in 5G WSN (EEC\u2010HAPCNN) is proposed for improved data transmission through the selected route. Initially, clustering is done by parallel adaptive canopy k\u2010means clustering (PaC\u2010k\u2010M) algorithm. Then, Tasmanian devil optimization algorithm (TDOA) selects the CH required for facilitating the high capacity and low latency features of 5G. The data are given to sink node through the selected CH utilizing hierarchical auto\u2010associative polynomial convolutional neural network (HAPCNN) for efficient routing in 5G wireless communication network. The proposed EEC\u2010HAPCNN method is implemented in NS\u20103 (network simulator 3). The proposed approach is examined using performance metrics like throughput, energy consumption, network lifetime, and number of nodes alive. The proposed EEC\u2010HAPCNN method provides 17.45%, 17.63%, and 18.43% lesser energy consumption and 17.64%, 17.64%, 18.54%, and 19.33% greater network life time compared with existing DBN\u2010MRFO\u20105G\u2010WSN, IDCNN\u2010t\u2010DSBO, DACP\u2010WSN\u2010ANN, EEO\u2010IWSN\u2010ML, and EECA\u2010ML\u2010WSN techniques.<\/jats:p>","DOI":"10.1002\/dac.6075","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:50:01Z","timestamp":1737334201000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Energy\u2010Efficient Communication Using Auto\u2010Associative Polynomial Convolutional Neural Network in WSN"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5826-2670","authenticated-orcid":false,"given":"Kanjoor\u00a0Vamanan","family":"Praveen","sequence":"first","affiliation":[{"name":"Department of Information Technology St. Peter's College of Engineering and Technology  Avadi Tamil Nadu India"}]},{"given":"Joe\u00a0Prathap\u00a0Pathrose","family":"Mary","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering R. M. D. Engineering College  Kavaraipettai Tamil Nadu India"}]},{"given":"Nagarajan","family":"Ramshankar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering R. M. D. Engineering College  Kavaraipettai Tamil Nadu India"}]},{"given":"Sundaram","family":"Murugesan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering R. M. D. Engineering College  Kavaraipettai Tamil Nadu India"}]}],"member":"311","published-online":{"date-parts":[[2025,1,19]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2021.01.004"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","unstructured":"J.Sun Q.Yu M.Niyazbek andF.Chu \u201cWITHDRAWN: 5G Network Information Technology and Military Information Communication Data Services \u201d2020.","DOI":"10.1016\/j.micpro.2020.103459"},{"key":"e_1_2_7_4_1","volume-title":"RF\u2010MEMS Technology for High\u2010Performance Passives","author":"Iannacci J.","year":"2022"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-022-09820-w"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3058636"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7901587"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/dac.4227"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21144798"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.suscom.2020.100504"},{"issue":"25","key":"e_1_2_7_11_1","first-page":"4G","article-title":"Efficient Secure Authentication Protocol for 5G Enabled Internet of Things Network","volume":"2020","author":"Kumar K. 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