Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment

Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT 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 Optimization based Data Aggregation and Fault Tolerance with Energy Management for Fog Enabled Heterogeneous IoT Environment P. Nalayini, P. Immaculate Rexi jenifer, V. Meena This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4191596/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 Fog computing is one of the efficient technologies used for overcoming the issues related to handling large quantities of Heterogeneous Internet of Things (HIoT) data like distributed network and health monitoring by dispensing the applications which gets applied nearer to the network edge. In fog based network model, distributed fog server closeness to the terminal devices allows currently available data to be transmitted effectually. But the increasing size of fog enabled HIoT environments leads to the design of effective routing and path selection to accomplish minimum delay and power consumption. With this motivation, this paper presents Optimization based Data Aggregation and fault Tolerance with Energy Management (ODFTEM) technique for fog enabled HIoT. This idea is segmented into three categories; they are a quasi-oppositional chimp optimization based data aggregation for routing, fault tolerance amongst the fog nodes and a Markov-chain-based probabilistic model for energy management. Initially, data aggregation is employed to gather the data and accumulate it for removing repetitive data and conserving energy. The goal of data aggregation is to reduce quantity of data dissemination and extend lifetime. A quasi-oppositional chimp optimization based data aggregation in routing is performed to choose an optimal collection of routes and perform data aggregation and it follows two stages like relay node selection and data aggregation. Secondly, to boost the network performance the best cost path is chosen where it selects the best nearest neighbor so that the packet transmission time can get reduced. Finally to forecast the energy consumption a Markov chain based probabilistic approach is used and this method can provide a comfortable energy saving model to the fog network. A detailed experimental validation of the ODFTEM technique reported the better performance in terms of energy consumption, throughput and transmission delay of the devices in the Fog enabled HIoT environment which is compared with the baseline methods like IHCFC, DAIFC and ODIFC. Heterogeneous Internet of things Fog environment Chimp optimization algorithm Route selection Fault Tolerance Energy Management and Markov Chain 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. 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-4191596","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289904260,"identity":"14374820-22b5-4f87-ae0e-cd541c6f0773","order_by":0,"name":"P. 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In fog based network model, distributed fog server closeness to the terminal devices allows currently available data to be transmitted effectually. But the increasing size of fog enabled HIoT environments leads to the design of effective routing and path selection to accomplish minimum delay and power consumption. With this motivation, this paper presents Optimization based Data Aggregation and fault Tolerance with Energy Management (ODFTEM) technique for fog enabled HIoT. This idea is segmented into three categories; they are a quasi-oppositional chimp optimization based data aggregation for routing, fault tolerance amongst the fog nodes and a Markov-chain-based probabilistic model for energy management.\u003c/p\u003e \u003cp\u003eInitially, data aggregation is employed to gather the data and accumulate it for removing repetitive data and conserving energy. The goal of data aggregation is to reduce quantity of data dissemination and extend lifetime. 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