A Model for Enabling Trustworthiness in V2V Networks
|University:||University of Pennsylvania|
|Principal Investigator:||Insup Lee|
|PI Contact Information:||3330 Walnut Street,602 Levine Hall, Department, of Computers and Information Science, University of Pennsylvania, Philadelphia, PA 19104 Phone : (215) 898-3532 FAX : (215) 573-7362 E-mail: email@example.com|
|Project Manager:||Courtney Ehrlichman|
|Funding Source(s) and Amounts Provided (by each agency or organization):||UTC|
|Agency ID/Contract/Grant Number:|
|Start and End Dates:||Start Date: May 2012|
End Date (anticipated): April 2014
|Abstract:||V2V and V2I networks are temporary, short-duration wireless networks designed for improving the overall driving experience by exchanging a multitude of information between vehicles and fixed infrastructure. However, given the presence of malicious entities, greedy drivers, and pranksters, blindly accepting any such information received (even one received through a cryptographically secured channel) can be catastrophic. In this project, we focus on building a model for managing (computing and maintaining) the trustworthiness of messages received over V2V networks.|
|Describe Implementation of Research Outcomes (or why not implemented):|
|Impacts/Benefits of Implementation (actual, not anticipated):||Our proposal can be used to determine the likelihood of the accuracy of V2V incident reports based on the trustworthiness of the originator of incident information and those vehicles that forward it. The proposed approach takes advantage of existing V2I communication facilities deployed and managed by central trafﬁc authorities, which can be used to collect vehicle behavior information in a crowd-sourcing fashion for constructing a more comprehensive view of vehicle trustworthiness.
For validating our scheme, we implemented a V2V/V2I trust simulator by extending an existing V2V simulator with trust management capabilities. Preliminary analysis of the model shows promising results. By combining our trust modeling technique with a simple threshold-based decision strategy, we observed on average 85% accuracy.
|Project URL:||Homepage located at Penn PRECISE research center: