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A Formal Method of Trust Computation in VANET: A Spatial, Temporal and Behavioral Approach

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Published
Publication date29/11/2022
Host publicationSmart Technologies in Urban Engineering
EditorsO. Arsenyeva, T. Romanova, M. Sukhonos, Y. Tsegelnyk
Place of PublicationCham
PublisherSpringer
Pages775-784
Number of pages10
ISBN (electronic)9783031201417
ISBN (print)9783031201400
<mark>Original language</mark>English

Publication series

NameSmart Technologies in Urban Engineering
PublisherSpringer International Publishing
Volume536
ISSN (Print)2367-3370
ISSN (electronic)2367-3389

Abstract

VANETs (vehicular Ad-hoc Networks) are a subclass of Mobile Ad-hoc Networks (MANETs) that are currently playing a key role in Intelligent Transportation Systems (ITS). A VANET is a vehicle-assisted network that develops a dynamic topology over time. VANETs are developed to assist two sorts of applications: safety and non-safety. Non-safety applications ensure that vehicle occupants travel in safety and have access to essential entertainment, whereas safety applications are designed to protect people and public property. The existing VANET requirements are concerned with privacy and security issues, but they don’t address how to assess the behavior of certified vehicles. A valid vehicle, for instance, could broadcast inaccurate data to a central monitoring system, causing the system to make an erroneous decision. Improved traffic safety and efficiency can be achieved via the use of VANET. However, in an untrusted environment, vehicles have difficulty determining the legitimacy of incoming messages. Trust, data accuracy, and dependability of data being broadcasted via the communication channel are the primary challenges in VANET. Depending on a variety of characteristics, vehicles can determine how trustworthy a given vehicle is based on how well it processes the received message. Therefore, a formal method of trust computation of vehicles is needed. The proposed framework is based on the spatial, temporal and behavioral paraments such as reputation, message correctness, participation degree, message similarity, message freshness, and vehicle age to compute the trust.