Open Access

ARTICLE

Decision Making in Internet of Vehicles Using Pervasive Trusted Computing Scheme

Geetanjali Rathee¹, Razi Iqbal^2,*, Adel Khelifi³

1 Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan, 173234, Himachal Pradesh, India
2 Department of Computer Information Systems, University of Fraser Valley, Canada
3 Department of Computer Science and IT, Abu Dhabi University, United Arab Emirates, UAE

* Corresponding Author: Razi Iqbal. Email:

(This article belongs to this Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)

Computers, Materials & Continua 2021, 68(2), 2755-2769. https://doi.org/10.32604/cmc.2021.017000

Received 18 January 2021; Accepted 28 February 2021; Issue published 13 April 2021

Abstract

Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention. Recently Internet of Vehicles (IoVs) has been introduced as one of the applications of pervasive computing that addresses the road safety challenges. Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues. Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data. Due to the lack of existing transportation integration schemes, IoV has not been completely explored by business organizations. In order to tackle this problem, we have proposed a novel trusted mechanism in IoV during communication, sensing, and record storing. Our proposed method uses trust based analysis and subjective logic functions with the aim of creating a trust environment for vehicles to communicate. In addition, the subjective logic function is integrated with multi-attribute SAW scheme to improve the decision metrics of authenticating nodes. The trust analysis depends on a variety of metrics to ensure an accurate identification of legitimate vehicles embedded with IoT devices ecosystem. The proposed scheme is determined and verified rigorously through various IoT devices and decision making metrics against a baseline solution. The simulation results show that the proposed scheme leads to 88% improvement in terms of better identification of legitimate nodes, road accidents and message alteration records during data transmission among vehicles as compared to the baseline approach.

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Citations