EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things

EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things | 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 EdgeTrust - A Lightweight Data-centric Trust Management Approach for Green Internet of Edge Things Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren, Hasan Ali Khattak, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-453986/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 Internet of Things (IoT) is bringing revolution into today’s world where devices in our surroundings become smart and perform daily-life activities and operations with more precision. The architecture of IoT is heterogeneous as it provides autonomy to nodes that they can communicate among other nodes and can also exchange information at any period. Due to the heterogeneous environment, IoT faces numerous security and privacy challenges, and one of the most significant challenges is the identification of malicious and compromised nodes. In this article, we have proposed a Machine Learning-based trust management approach for edge nodes. The proposed approach is a lightweight process to evaluate trust because edge nodes cannot perform complex computations. To evaluate trust, the proposed mechanism utilizes the knowledge and experience component of trust where knowledge is further based on several parameters. To eliminate the triumphant execution of good and bad-mouthing attacks, the proposed approach utilizes edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit where only those that satisfy the threshold value can participate in the network. To validate the performance of a proposed approach we have performed an extensive simulation in comparison with the existing approaches and the result shows the effectiveness of the proposed approach against several potential attacks. Technical Communication Internet of Things Trust Management Machine Learning Deep Neural Networks Malicious Nodes IoT Attacks Security Privacy Preservation. Full Text Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF. 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-453986","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":30833619,"identity":"f90a571f-072d-45cc-8ceb-7d84972c00fd","order_by":0,"name":"Kamran Ahmad Awan","email":"","orcid":"","institution":"The University of Haripur","correspondingAuthor":false,"prefix":"","firstName":"Kamran","middleName":"Ahmad","lastName":"Awan","suffix":""},{"id":30833620,"identity":"6203f362-e890-400b-a9e7-859be134dd69","order_by":1,"name":"Ikram Ud Din","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACNh4wxczYxsB84PCPCgYGAxK0sCU+ZjhDhBYGmJYGBh5jY6BGwlr4eM4+/HSzzVq2T7rBTLpw3mF5c/bmAww/Krbhdhhvu7F0blu6cZvMgTTpmdsOG+7sOZbA2HPmNm4t/GwMQC2HE9skEo5J8G47zLjhRo4B0Gt4tTD/hmgBIt45h+0Ja+FtY4PaksxszNtwOJGwFp5jbNY554B+kUhjfDjjWHryhjPHEg7i84t8Txrz7Zwya9n5M/I/HPhQY2274XjzwQc/KnBrQQfNYPIA0eqBoI4UxaNgFIyCUTBCAABQGlgyADIu5QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8896-547X","institution":"The University of Haripur","correspondingAuthor":true,"prefix":"","firstName":"Ikram","middleName":"Ud","lastName":"Din","suffix":""},{"id":30833621,"identity":"6b660244-2a0c-4294-b08e-a591706bae87","order_by":2,"name":"Ahmad Almogren","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Almogren","suffix":""},{"id":30833622,"identity":"56c8dc3a-6e21-45f3-a4a5-6e8ee9625ef2","order_by":3,"name":"Hasan Ali Khattak","email":"","orcid":"","institution":"NUST: National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"Ali","lastName":"Khattak","suffix":""},{"id":30833623,"identity":"21d5a29d-50f9-4a3c-9a34-c3782a80e9cd","order_by":4,"name":"Joel J.P.C. 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The architecture of IoT is heterogeneous as it provides autonomy to nodes that they can communicate among other nodes and can also exchange information\u0026nbsp;at any period. Due to the heterogeneous environment, IoT\u0026nbsp;faces numerous security and privacy challenges, and one\u0026nbsp;of the most significant challenges is the identification of\u0026nbsp;malicious and compromised nodes. In this article, we have\u0026nbsp;proposed a Machine Learning-based trust management approach for edge nodes. The proposed approach is a lightweight\u0026nbsp;process to evaluate trust because edge nodes cannot perform complex computations. To evaluate trust, the proposed\u0026nbsp;mechanism utilizes the knowledge and experience component of trust where knowledge is further based on several\u0026nbsp;parameters. To eliminate the triumphant execution of good and bad-mouthing attacks, the proposed approach utilizes\u0026nbsp;edge clouds, i.e., local data centers, to collect recommendations to evaluate indirect and aggregated trust. The trustworthiness of nodes is ranked between a certain limit where\u0026nbsp;only those that satisfy the threshold value can participate in\u0026nbsp;the network. 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