Resilient Geospatial Data Management: A Comparative Analysis of Cloud-Native and Distributed Ledger Technology Synchronization Models for Multi-Cloud Environments

Resilient Geospatial Data Management: A Comparative Analysis of Cloud-Native and Distributed Ledger Technology Synchronization Models for Multi-Cloud Environments | 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 Resilient Geospatial Data Management: A Comparative Analysis of Cloud-Native and Distributed Ledger Technology Synchronization Models for Multi-Cloud Environments Oluwafemi Oloruntoba, Princewill Odum, Sheriff Adepoju, Khadijah Audu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6523745/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study presents a comparative analysis of cloud-native and Distributed Ledger Technology (DLT)-based synchronization models for resilient geospatial data management in multi-cloud environments. With the rising demand for real-time geospatial data in applications such as smart cities, disaster response, and environmental monitoring, ensuring data consistency, availability, and integrity across distributed cloud infrastructures has become increasingly critical. Cloud-native models offer high throughput and scalability through managed replication and consistency protocols but may be limited by eventual consistency and reliance on provider-managed security. In contrast, DLT-based models, particularly those using blockchain, enhance data integrity and auditability through decentralized, tamper-proof synchronization, albeit at the cost of increased latency and operational complexity. To evaluate these trade-offs, we propose a composite performance framework encompassing resilience, synchronization efficiency, and operational cost. Using simulation-based analysis, we assess both models under various failure scenarios and performance conditions. Results highlight the strengths and limitations of each approach and underscore the value of a hybrid model—combining the speed of cloud-native systems with the trust guarantees of DLT—for mission-critical geospatial applications. This research offers practical recommendations for system designers and contributes to the evolving integration of blockchain, cloud, and AI technologies in secure, multi-cloud geospatial infrastructures. Geospatial Data Management Multi-Cloud Architecture Cloud-Native Synchronization Distributed Ledger Technology Blockchain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviews received at journal 22 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 26 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 24 Apr, 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. 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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-6523745","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472656691,"identity":"d783231b-6d81-4c6e-b290-e34ffbefc6df","order_by":0,"name":"Oluwafemi 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