Mobility and Fault Tolerance-Based IoT Task Offloading and Scheduling for Intelligent Transportation Systems Using a Fog–Cloud Hybrid Environment

Mobility and Fault Tolerance-Based IoT Task Offloading and Scheduling for Intelligent Transportation Systems Using a Fog–Cloud Hybrid Environment | 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 Mobility and Fault Tolerance-Based IoT Task Offloading and Scheduling for Intelligent Transportation Systems Using a Fog–Cloud Hybrid Environment Nihar Karra, Nikhil Karra, Goutham Bandapati, Louis Echefu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7602694/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Intelligent Transportation Systems (ITS) uses IoT devices to communicate with home servers. With increasing number of IoT devices the ITS need to offload and schedule the huge amount of data cloud and fog. The data are divided into delay sensitive and computation intensive moods. Due to the vast variety of data and the importance of the data the researchers introduced the IoT task of offloading and scheduling to meet certain QoS. Due to various factors such as vehicles mobility, directions, node energy, and processing requirement the system becomes NP hard. To address the above limitations, we have proposed a fog–cloud collaboration system that reduce the above limitations. In the proposed model we used the Analytic Hierarchy Process (AHP) to organize tasks based on different factors, such as their size, the amount of processing power they need, and the priority of tasks. The proposed model decides whether to execute the tasks locally or to offload to cloud or fog nodes. The proposed model consists of three main algorithms, The first algorithm is used for offloading decisions while considering vehicles mobility, task size, processing power required and other factors. The second algorithm is used to prioritize tasks as delay sensitive and computation intensive by using Analytical Hierarchy Process (AHP) while considering threshold values. Algorithm 2 decide and provide two queues list one is for offloading fog nodes while the queue list for offloading to cloud nodes. The third algorithm is used to allocate tasks to the most efficient fog nodes and cloud nodes based on the received queues. We also prioritize fog nodes based on node energy, ram size, processing power, and current load. After successful allocation to fog node the proposed system, also uses a fault tolerance mechanism to check and monitor the task execution. This framework can assign backup nodes in situations where failures are more common and can replicate important tasks because of this clustering. To test the framework under various conditions, including different workloads, node density, and failure scenarios, we performed extensive simulations using OMNeT++. The proposed model reduced the tasks delays by up to 28. 4% and energy usage by about 31. 6% compared to other models like DCP, FADEC, and a few others. Also, it was found that when the system is under heavy load, the success rate of tasks increased by 9–12%. Based on these results, the model is well-suited for situations where reliability and fast response times are important, such as in smart healthcare, transportation, and industrial settings. Fog–Cloud Computing AHP-based Prioritization Task Scheduling Adaptive Offloading Intelligent Transportation Mobility Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Mar, 2026 Reviews received at journal 19 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviews received at journal 10 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 17 Sep, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 12 Sep, 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. 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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-7602694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519725935,"identity":"cb417623-0367-4818-aefc-30f63d6f22c0","order_by":0,"name":"Nihar Karra","email":"","orcid":"","institution":"Amazon Web Services","correspondingAuthor":false,"prefix":"","firstName":"Nihar","middleName":"","lastName":"Karra","suffix":""},{"id":519725937,"identity":"5dd13b7c-1d2a-4862-9787-d519fb286ecf","order_by":1,"name":"Nikhil 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fog–Cloud Computing, AHP-based Prioritization, Task Scheduling, Adaptive Offloading, Intelligent Transportation, Mobility","lastPublishedDoi":"10.21203/rs.3.rs-7602694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7602694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntelligent Transportation Systems (ITS) uses IoT devices to communicate with home servers. With increasing number of IoT devices the ITS need to offload and schedule the huge amount of data cloud and fog. The data are divided into delay sensitive and computation intensive moods. Due to the vast variety of data and the importance of the data the researchers introduced the IoT task of offloading and scheduling to meet certain QoS. Due to various factors such as vehicles mobility, directions, node energy, and processing requirement the system becomes NP hard. To address the above limitations, we have proposed a fog\u0026ndash;cloud collaboration system that reduce the above limitations. In the proposed model we used the Analytic Hierarchy Process (AHP) to organize tasks based on different factors, such as their size, the amount of processing power they need, and the priority of tasks. The proposed model decides whether to execute the tasks locally or to offload to cloud or fog nodes. The proposed model consists of three main algorithms, The first algorithm is used for offloading decisions while considering vehicles mobility, task size, processing power required and other factors. The second algorithm is used to prioritize tasks as delay sensitive and computation intensive by using Analytical Hierarchy Process (AHP) while considering threshold values. Algorithm 2 decide and provide two queues list one is for offloading fog nodes while the queue list for offloading to cloud nodes. The third algorithm is used to allocate tasks to the most efficient fog nodes and cloud nodes based on the received queues. We also prioritize fog nodes based on node energy, ram size, processing power, and current load. After successful allocation to fog node the proposed system, also uses a fault tolerance mechanism to check and monitor the task execution. This framework can assign backup nodes in situations where failures are more common and can replicate important tasks because of this clustering. To test the framework under various conditions, including different workloads, node density, and failure scenarios, we performed extensive simulations using OMNeT++. The proposed model reduced the tasks delays by up to 28. 4% and energy usage by about 31. 6% compared to other models like DCP, FADEC, and a few others. Also, it was found that when the system is under heavy load, the success rate of tasks increased by 9\u0026ndash;12%. Based on these results, the model is well-suited for situations where reliability and fast response times are important, such as in smart healthcare, transportation, and industrial settings.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Mobility and Fault Tolerance-Based IoT Task Offloading and Scheduling for Intelligent Transportation Systems Using a Fog–Cloud Hybrid Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 16:56:20","doi":"10.21203/rs.3.rs-7602694/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-15T12:00:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T15:42:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-17T00:42:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297036204074025796634517980948176806752","date":"2025-10-10T15:28:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T06:57:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97844261728235995813952350725102002435","date":"2025-10-08T07:03:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118823484382309596487666733727536939197","date":"2025-10-07T07:08:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T04:44:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266034389716954029258498203482964274683","date":"2025-09-18T05:34:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-17T05:54:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T05:53:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T14:21:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cluster Computing","date":"2025-09-12T17:29:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"94430eb7-b69a-4ca4-ab52-2fc25c7105a9","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-15T12:09:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 16:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7602694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7602694","identity":"rs-7602694","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}