Version 1
: Received: 9 July 2024 / Approved: 10 July 2024 / Online: 10 July 2024 (05:08:37 CEST)
How to cite:
Wang, Y.; Mahmood, A.; Mohd Sabri, M. F.; Zen, H. TM – IoV : A Dataset for the Trust Management in the Internet of Vehicles. Preprints2024, 2024070813. https://doi.org/10.20944/preprints202407.0813.v1
Wang, Y.; Mahmood, A.; Mohd Sabri, M. F.; Zen, H. TM – IoV : A Dataset for the Trust Management in the Internet of Vehicles. Preprints 2024, 2024070813. https://doi.org/10.20944/preprints202407.0813.v1
Wang, Y.; Mahmood, A.; Mohd Sabri, M. F.; Zen, H. TM – IoV : A Dataset for the Trust Management in the Internet of Vehicles. Preprints2024, 2024070813. https://doi.org/10.20944/preprints202407.0813.v1
APA Style
Wang, Y., Mahmood, A., Mohd Sabri, M. F., & Zen, H. (2024). TM – IoV : A Dataset for the Trust Management in the Internet of Vehicles. Preprints. https://doi.org/10.20944/preprints202407.0813.v1
Chicago/Turabian Style
Wang, Y., Mohamad Faizrizwan Mohd Sabri and Hushairi Zen. 2024 "TM – IoV : A Dataset for the Trust Management in the Internet of Vehicles" Preprints. https://doi.org/10.20944/preprints202407.0813.v1
Abstract
The emerging and promising paradigm of the Internet of Vehicles (IoV) employ vehicle-to-everything communication for facilitating vehicles to not only communicate with one another but also with the supporting roadside infrastructure, vulnerable pedestrians, and the backbone network in a bid to primarily address a number of safety-critical vehicular applications. Nevertheless, owing to the inherent characteristics of IoV networks, in particular, of being (a) highly dynamic in nature and which results in a continual change in the network topology and (b) non-deterministic owing to the intricate nature of its entities and their interrelationships, they are susceptible to a number of malicious attacks. Such sort of attacks, if and when materializes, jeopardizes the entire IoV network, thereby putting the human lives at risk. Whilst the cryptographic-based mechanisms are capable of mitigating the external attacks, the internal attacks are extremely hard to tackle. Trust, therefore, is an indispensable tool since it facilitates in the timely identification and eradication of malicious entities responsible for launching internal attacks in an IoV network. To date, there is no dataset pertinent to trust management in the context of IoV networks and the same has proven to be a bottleneck for conducting an in-depth research in this domain. The manuscript-at-hand, accordingly, presents a first of its kind trust-based IoV dataset encompassing 96,707 interactions amongst 79 vehicles at different time instances. The dataset involves 9 salient trust parameters, i.e., packet delivery ratio, similarity, external similarity, internal similarity, familiarity, external familiarity, internal familiarity, reward / punishment, and context, which play a considerable role for ascertaining the trust of a vehicle within an IoV network.
Keywords
Internet of Vehicles; malicious behavior; trust management; trust-based IoV simulator; trust parameters
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
