Energy-efficient Optimization Data Collection Algorithm based on Mobile Edge Sensing in 5G Underwater Internet of Things

Energy-efficient Optimization Data Collection Algorithm based on Mobile Edge Sensing in 5G Underwater Internet of Things | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Energy-efficient Optimization Data Collection Algorithm based on Mobile Edge Sensing in 5G Underwater Internet of Things Xiaoyun Guang, Cao Lei, Liu Chunfeng, Zhao Zhao, Qu Wenyu, Li Mingyue, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6868579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Journal on Wireless Communications and Networking → Version 1 posted You are reading this latest preprint version Abstract Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge sensing devices (such as autonomous underwater vehicles (AUVs)) provide a promising approach for data collection from sensor nodes. The deployment of 5G technology in UIoT signifies a significant advancement in underwater network communication capabilities. However, UIoT is severely affected by the underwater dynamic environment and the limited energy of AUV. For instance, node mobility caused network instability, affecting data collection efficiency. And high and uneven energy consumption leads to shortened network lifetime. Moreover, limited AUV energy results in AUV loss and diminished data collection efficiency. To solve this problems, an energy-efficient optimization data collection algorithm based on mobile edge sensing in 5G underwater internet of things (EEODC-MES) is proposed in this paper. In EEODC-MES, the network clustering is constructed by analyzing the movement characteristics of sensor nodes, and a cluster-head node is selected. Subsequently, the reward for edge sensing device (AUV) collecting data from cluster-head nodes is calculated based on the payoff matrix. The cluster-head node with the highest reward value is prioritized for AUV visitation. The performance of EEODC-MES is compared with that of other data collection algorithms, namely GAAP, AEEDCO, and TSP. Compared with GAAP, AEEDCO, and TSP, EEODC-MES respectively improves the network lifetime by 31.8%, 30.1% and 7.1%. Compared with GAAP and TSP, EEODC-MES respectively reduces the collection delay by 26.08% and 51.77%. UIoT data collection energy-efficient optimization AUV node mobility Full Text Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in Journal on Wireless Communications and Networking → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6868579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498255260,"identity":"ca65510a-3041-443b-9895-7e3389d202fc","order_by":0,"name":"Xiaoyun 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mobility","lastPublishedDoi":"10.21203/rs.3.rs-6868579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6868579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge sensing devices (such as autonomous underwater vehicles (AUVs)) provide a promising approach for data collection from sensor nodes. The deployment of 5G technology in UIoT signifies a significant advancement in underwater network communication capabilities. However, UIoT is severely affected by the underwater dynamic environment and the limited energy of AUV. For instance, node mobility caused network instability, affecting data collection efficiency. And high and uneven energy consumption leads to shortened network lifetime. Moreover, limited AUV energy results in AUV loss and diminished data collection efficiency. To solve this problems, an energy-efficient optimization data collection algorithm based on mobile edge sensing in 5G underwater internet of things (EEODC-MES) is proposed in this paper. In EEODC-MES, the network clustering is constructed by analyzing the movement characteristics of sensor nodes, and a cluster-head node is selected. Subsequently, the reward for edge sensing device (AUV) collecting data from cluster-head nodes is calculated based on the payoff matrix. The cluster-head node with the highest reward value is prioritized for AUV visitation. The performance of EEODC-MES is compared with that of other data collection algorithms, namely GAAP, AEEDCO, and TSP. Compared with GAAP, AEEDCO, and TSP, EEODC-MES respectively improves the network lifetime by 31.8%, 30.1% and 7.1%. Compared with GAAP and TSP, EEODC-MES respectively reduces the collection delay by 26.08% and 51.77%.","manuscriptTitle":"Energy-efficient Optimization Data Collection Algorithm based on Mobile Edge Sensing in 5G Underwater Internet of Things","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 06:48:07","doi":"10.21203/rs.3.rs-6868579/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aea11730-bbd6-4458-84bd-fb7f09a295e0","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:00:11+00:00","versionOfRecord":{"articleIdentity":"rs-6868579","link":"https://doi.org/10.1186/s13638-026-02579-9","journal":{"identity":"journal-on-wireless-communications-and-networking","isVorOnly":true,"title":"Journal on Wireless Communications and Networking"},"publishedOn":"2026-02-28 15:57:17","publishedOnDateReadable":"February 28th, 2026"},"versionCreatedAt":"2025-08-14 06:48:07","video":"","vorDoi":"10.1186/s13638-026-02579-9","vorDoiUrl":"https://doi.org/10.1186/s13638-026-02579-9","workflowStages":[]},"version":"v1","identity":"rs-6868579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6868579","identity":"rs-6868579","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}