Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Service vs Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment

Singh, Parminder and Kaur, Avinash and Batth, Ranbir Singh and Aujla, Gagangeet Singh and Masud, Mehedi (2021) 'Service vs Protection: A Bayesian Learning Approach for Trust Provisioning in Edge of Things Environment.', IEEE Internet of Things Journal .

Abstract

Edge of Things (EoT) technology enables end-users participation with smart-sensors and mobile devices (such as smartphones, wearable devices) to the smart devices across the smart city. Trust management is the main challenge in EoT infrastructure to consider the trusted participants. The Quality of Service (QoS) is highly affected by malicious users with fake or altered data. In this paper, a Robust Trust Management (RTM) scheme is designed based on Bayesian learning and collaboration filtering. The proposed RTM model is regularly updated after a specific interval with the significant decay value to the current calculated scores to update the behavior changes quickly. The dynamic characteristics of edge nodes are analyzed with the new probability score mechanism from recent services’ behavior. The performance of the proposed trust management scheme is evaluated in a simulated environment. The percentage of collaboration devices are tuned as 10%, 50% and 100%. The maximum accuracy of 99.8% is achieved from the proposed RTM scheme. The experimental results demonstrate that the RTM scheme shows better performance than the existing techniques in filtering malicious behavior and accuracy.

Item Type:Article
Full text:(AM) Accepted Manuscript
Download PDF
(362Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/JIOT.2021.3082272
Publisher statement:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:20 September 2021
Date of first online publication:21 May 2021
Date first made open access:20 September 2021

Save or Share this output

Export:
Export
Look up in GoogleScholar