Federated Edge-AI for Reliable and Privacy-Preserving Pipeline Leak Detection in Drone Swarms Using Neutrosophic Sugeno-Weber Norms

Federated Edge-AI for Reliable and Privacy-Preserving Pipeline Leak Detection in Drone Swarms Using Neutrosophic Sugeno-Weber Norms | 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 Article Federated Edge-AI for Reliable and Privacy-Preserving Pipeline Leak Detection in Drone Swarms Using Neutrosophic Sugeno-Weber Norms Rana Muhammad Zulqarnain, Muhammad Shazib Hameed, Eman Noreen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8232308/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract The ability to monitor the safety of natural gas pipelines is guaranteed by leak detection. Systems are capable of responding quickly and very precisely to events because delays during such events can lead to serious environmental consequences, hurt, damage, or even danger. Federated is a special framework that exists in this work. Leak Detection of Natural Gas pipelines Edge-A-enabled autonomous drone swarms will be real-time, where smart drones will be able to cooperate and reduce latency, keep sensitive data, and improve detection of anomalies in dynamic operational conditions. Such complex decentralized control systems also need advanced systems of decision-making that are capable of dealing with uncertainty, shifting goals, and information gaps or ambiguous information. The research will, in an attempt to achieve this, emphasize the Multi-Criteria Decision-Making (MCDM) methods that have been used over the years as an alternative method of analysis, which is systematic and founded on alternative performance measures. The precedent versions of MCDM, which applied the theory of fuzzy set, allowed the analysts to convey their judgments with vagueness and partial truth. As uncertainty and conflicting decision environments increased, however, neutrosophic sets were included to describe the degree of truth, falsity, and indeterminacy on their own. This was a later representation that was refined to describe hesitation more by using ambiguous membership and non-membership functions of intuitionistic fuzzy sets (IFS). The combined paradigms led to Intuitionistic Neutrosophic Set (INS), a paradigm of powerful mathematics that can reflect the complexity and ambiguity of the decision-making problems of the real world. In this research, the INS framework is isused with Sugeno-Weber (SW) aggregation operators to come up with a hybrid DM framework that is optimally designed to respond to the real-time leak detection and assessment in pipeline networks. The proposed INS-SW solution is contrasted with the time-tested approach to the evaluation of performance, the Weighted Aggregated Sum Product Assessment (WASPAS), as it is easy to operate and can generate credible ranks. The comparative outcomes indicate that the INS-SW model can be better adapted to uncertain, interdependent, and dynamic operation environments and is more robust and precise in that case. In general, the results suggest that the suggested framework adds to the fact and veracity of the drone-based leakage detection to a substantial degree and can provide a scalable and intelligent decision-making tool related to the imperative energy infrastructure. Besides this application in specific, the paper would also applicable in developing uncertainty-sensitive decision science further, besides offering an insight into how to develop sustainable, intelligent, and resilient energy systems in future industrial processes Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files fig01.png fig02.png pic3.png Cite Share Download PDF Status: Published Journal Publication published 16 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Submission checks completed at journal 01 Dec, 2025 First submitted to journal 01 Dec, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8232308","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":553592673,"identity":"7ee94489-dd73-4dc0-9915-ab35eb56ba93","order_by":0,"name":"Rana Muhammad Zulqarnain","email":"","orcid":"","institution":"School of Business, Xian International University, Xian, 710077, Shaanxi, China","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"Muhammad","lastName":"Zulqarnain","suffix":""},{"id":553592675,"identity":"d981f344-7f26-4c39-99db-1ae9cfa2b6f2","order_by":1,"name":"Muhammad Shazib Hameed","email":"data:image/png;base64,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","orcid":"","institution":"Institute of Mathematics, Khwaja Fareed University of Engineering \\\u0026 Information Technology, Rahim Yar Khan 64200, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Shazib","lastName":"Hameed","suffix":""},{"id":553592676,"identity":"aeea0530-6e7a-499e-96c6-d60e607013d9","order_by":2,"name":"Eman Noreen","email":"","orcid":"","institution":"Institute of Mathematics, Khwaja Fareed University of Engineering \\\u0026 Information Technology, Rahim Yar Khan 64200, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Eman","middleName":"","lastName":"Noreen","suffix":""},{"id":553592681,"identity":"58b962f8-e49d-4652-8935-1b4c3be21277","order_by":3,"name":"Muntazim Abbas Hashmi","email":"","orcid":"","institution":"Institute of Mathematics, Khwaja Fareed University of Engineering \\\u0026 Information Technology, Rahim Yar Khan 64200, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muntazim","middleName":"Abbas","lastName":"Hashmi","suffix":""},{"id":553592683,"identity":"578823aa-ad84-42e1-948f-6323161e2fc1","order_by":4,"name":"Dragan Pamu{\\v{c}}ar","email":"","orcid":"","institution":"Sustainability Competence Centre, Széchenyi István University, Gyor, Hungary","correspondingAuthor":false,"prefix":"","firstName":"Dragan","middleName":"","lastName":"Pamu{\\v{c}}ar","suffix":""},{"id":553592686,"identity":"0e978da3-cb06-42a5-b691-ae4c3bf25c6e","order_by":5,"name":"Muhammad Kamran","email":"","orcid":"","institution":"Research Institute of Business Analytics and Supply Chain Management, College of Management, Shenzhen University, Shenzhen, China","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Kamran","suffix":""},{"id":553592687,"identity":"5f753c00-b71b-47ae-a077-ab28dc5a465b","order_by":6,"name":"Vladimir Simic","email":"","orcid":"","institution":"Department of Computer Science and Engineering, College of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Simic","suffix":""}],"badges":[],"createdAt":"2025-11-28 17:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8232308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8232308/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-42794-4","type":"published","date":"2026-03-16T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97219670,"identity":"b6ca0ea7-a2bd-4cfd-b8c7-7eaa29de32bf","added_by":"auto","created_at":"2025-12-02 07:13:16","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10172,"visible":true,"origin":"","legend":"","description":"","filename":"7f09ddad776c423ba11a36c1bcf87f09.json","url":"https://assets-eu.researchsquare.com/files/rs-8232308/v1/52facba462b04a63c4601dd8.json"},{"id":105224800,"identity":"c42b696f-e769-421c-8001-0a6b8c652c08","added_by":"auto","created_at":"2026-03-23 16:16:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":767297,"visible":true,"origin":"","legend":"","description":"","filename":"uploaded.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8232308/v1_covered_36120918-fda9-45f3-9cc4-221d752cde39.pdf"},{"id":97219672,"identity":"2f8fd34c-0219-4650-af4f-66504c3336a7","added_by":"auto","created_at":"2025-12-02 07:13:16","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":156482,"visible":true,"origin":"","legend":"","description":"","filename":"fig01.png","url":"https://assets-eu.researchsquare.com/files/rs-8232308/v1/9cdbd0e9d7582f441de4c406.png"},{"id":97219669,"identity":"6bdca865-2cfe-4439-b81b-d06733da5502","added_by":"auto","created_at":"2025-12-02 07:13:16","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51744,"visible":true,"origin":"","legend":"","description":"","filename":"fig02.png","url":"https://assets-eu.researchsquare.com/files/rs-8232308/v1/ef58f7734f2da472e364d833.png"},{"id":97250377,"identity":"59922e56-0b5a-4a69-ae44-058bd134c959","added_by":"auto","created_at":"2025-12-02 13:14:23","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":105352,"visible":true,"origin":"","legend":"","description":"","filename":"pic3.png","url":"https://assets-eu.researchsquare.com/files/rs-8232308/v1/88df9c4259209d786fe33a60.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Federated Edge-AI for Reliable and Privacy-Preserving Pipeline Leak Detection in Drone Swarms Using Neutrosophic Sugeno-Weber Norms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8232308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8232308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The ability to monitor the safety of natural gas pipelines is guaranteed by leak detection. Systems are capable of responding quickly and very precisely to events because delays during such events can lead to serious environmental consequences, hurt, damage, or even danger. Federated is a special framework that exists in this work. Leak Detection of Natural Gas pipelines Edge-A-enabled autonomous drone swarms will be real-time, where smart drones will be able to cooperate and reduce latency, keep sensitive data, and improve detection of anomalies in dynamic operational conditions. Such complex decentralized control systems also need advanced systems of decision-making that are capable of dealing with uncertainty, shifting goals, and information gaps or ambiguous information. The research will, in an attempt to achieve this, emphasize the Multi-Criteria Decision-Making (MCDM) methods that have been used over the years as an alternative method of analysis, which is systematic and founded on alternative performance measures. The precedent versions of MCDM, which applied the theory of fuzzy set, allowed the analysts to convey their judgments with vagueness and partial truth. As uncertainty and conflicting decision environments increased, however, neutrosophic sets were included to describe the degree of truth, falsity, and indeterminacy on their own. This was a later representation that was refined to describe hesitation more by using ambiguous membership and non-membership functions of intuitionistic fuzzy sets (IFS). The combined paradigms led to Intuitionistic Neutrosophic Set (INS), a paradigm of powerful mathematics that can reflect the complexity and ambiguity of the decision-making problems of the real world. In this research, the INS framework is isused with Sugeno-Weber (SW) aggregation operators to come up with a hybrid DM framework that is optimally designed to respond to the real-time leak detection and assessment in pipeline networks. The proposed INS-SW solution is contrasted with the time-tested approach to the evaluation of performance, the Weighted Aggregated Sum Product Assessment (WASPAS), as it is easy to operate and can generate credible ranks. The comparative outcomes indicate that the INS-SW model can be better adapted to uncertain, interdependent, and dynamic operation environments and is more robust and precise in that case. In general, the results suggest that the suggested framework adds to the fact and veracity of the drone-based leakage detection to a substantial degree and can provide a scalable and intelligent decision-making tool related to the imperative energy infrastructure. Besides this application in specific, the paper would also applicable in developing uncertainty-sensitive decision science further, besides offering an insight into how to develop sustainable, intelligent, and resilient energy systems in future industrial processes","manuscriptTitle":"Federated Edge-AI for Reliable and Privacy-Preserving Pipeline Leak Detection in Drone Swarms Using Neutrosophic Sugeno-Weber Norms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 07:13:12","doi":"10.21203/rs.3.rs-8232308/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T17:11:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-01T14:28:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-01T13:59:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69258310-dfd3-464a-8ed2-bbce882fda11","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":58908067,"name":"Physical sciences/Engineering"},{"id":58908068,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-03-23T16:14:31+00:00","versionOfRecord":{"articleIdentity":"rs-8232308","link":"https://doi.org/10.1038/s41598-026-42794-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-16 15:57:44","publishedOnDateReadable":"March 16th, 2026"},"versionCreatedAt":"2025-12-02 07:13:12","video":"","vorDoi":"10.1038/s41598-026-42794-4","vorDoiUrl":"https://doi.org/10.1038/s41598-026-42794-4","workflowStages":[]},"version":"v1","identity":"rs-8232308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8232308","identity":"rs-8232308","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}