A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization

A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization | 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 A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization Michael Wilson, Henry Nunoo-Mensah, Kwame Osei Boateng, Francisca Adoma Acheampong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4625359/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract IoT devices relying on cloud-based computing resources for data processing are causing unsustainable growth and latency, thus making their dependence on these computing resources a foremost challenge to real-time and time-critical IoT applications. Researchers have proffered several approaches, but notable amongst them are the bio-inspired optimisation methods due to their ability to model stochastic environments efficiently. This paper proposes a bio-inspired load-balancing optimisation approach for reducing latency in IoT networks. The paper leverages the concept of “stigmergy” in social ants to create a metric for assessing the computational capacity, reliability, and availability of a node’s computing resources in a decentralised Internet of Things (IoT) network. The main contribution of this work is the design of a decentralised serverless computing paradigm for stateful(reliable) task offloading and data processing among end-user nodes in an IoT environment. A novel bio-inspired algorithm is designed for latency reduction and optimal load-balancing in any collaborative data processing environment without a centralised server. This work also introduces the concept of computing pheromones as a metric for assessing computation resource capacity and availability in decentralised data processing environments, which paves the way for more efficient and reliable IoT data processing solutions. Experimental results show the effectiveness of the proposed IoT data processing approach, highlighting improvements in response time and turnaround time compared to existing approaches. Task Offloading IoT/Edge interplay scheduling in IoT networks Computational resource allocation IoT data processing Latency reduction server-less computing Mobile Edge Computing (MEC) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-4625359","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330506427,"identity":"f9e4aa97-235b-4ca8-9b3e-6a269098a696","order_by":0,"name":"Michael Wilson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RMQrCMBSA4dfFqZI1k55AqAiO7VVe6dCpk0vHSCGT0LWXEDxCSoYuOUBBUYrgKB3rZiOCuDR0E8k/hCTwQR4BsNl+Mmcr+nXWb9j7xjUSpslqDAHQJGSfs4EsioiVXXqK81zyFtKzz8heDJJ1HTLpqltS1GFWgNpEjN5xmBx74nCZHKiTgcMxAqo8IykfXMYeKUcQMeUSPQhfxAeyM5BLo2eRSz0LoEKcUNdAVNU0XSrnJK+u0KYYEGJ42HfYfxCnOELoAiBiJLHZbLZ/7wn/D01h+6vUqQAAAABJRU5ErkJggg==","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"","lastName":"Wilson","suffix":""},{"id":330506428,"identity":"64f09314-1c6c-457c-b492-2127b23c877a","order_by":1,"name":"Henry Nunoo-Mensah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Nunoo-Mensah","suffix":""},{"id":330506429,"identity":"a49755d9-0f84-4ff6-b047-b1dba67fab6c","order_by":2,"name":"Kwame Osei Boateng","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kwame","middleName":"Osei","lastName":"Boateng","suffix":""},{"id":330506430,"identity":"a41029cd-7d2f-44e6-84e3-c1088710538b","order_by":3,"name":"Francisca Adoma Acheampong","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Francisca","middleName":"Adoma","lastName":"Acheampong","suffix":""}],"badges":[],"createdAt":"2024-06-23 13:21:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4625359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4625359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61374961,"identity":"cf34c09d-7b79-40a4-9b2b-81da1fc5e09e","added_by":"auto","created_at":"2024-07-30 04:05:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1898654,"visible":true,"origin":"","legend":"","description":"","filename":"ARealTimeTaskBalancingStrategyforIoTNetworksUsingAntColonyOptimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4625359/v1_covered_0db84dc6-90b4-475e-bfbf-73b789bb47f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Real-Time Task Balancing Strategy for IoT Networks Using Ant Colony Optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Task Offloading, IoT/Edge interplay, scheduling in IoT networks, Computational resource allocation, IoT data processing, Latency reduction, server-less computing, Mobile Edge Computing (MEC)","lastPublishedDoi":"10.21203/rs.3.rs-4625359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4625359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"IoT devices relying on cloud-based computing resources for data processing are causing unsustainable growth and latency, thus making their dependence on these computing resources a foremost challenge to real-time and time-critical IoT applications. 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