6,654 research outputs found
A Trust Model for Vehicular Network-Based Incident Reports
Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) networks are ephemeral, short-duration wireless networks that have the potential to improve the overall driving experience through the exchange of information between vehicles. V2V and V2I networks operate primarily by distributing real-time incident reports regarding potential traffic problems such as traffic jams, accidents, bad roads and so on to other vehicles in their vicinity over a multi-hop network. However, given the presence of malicious entities, blindly trusting such incident reports (even the one received through a cryptographically secure channel) can lead to undesirable consequences. In this paper, we propose an approach to determine the likelihood of the accuracy of V2V incident reports based on the trustworthiness of the report originator and those vehicles that forward it. The proposed approach takes advantage of existing road-side units (RSU) based V2I communication infrastructure deployed and managed by central traffic authorities, which can be used to collect vehicle behavior information in a crowd-sourcedfashion 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 threshold-based decision strategy, we observed on average 85% accuracy
Trust in Vehicle-to-Vehicle Communication
In traditional Pedestrian Automatic Emergency Braking (PAEB) system, vehicles equipped with onboard sensors such as radar, camera, and infrared detect pedestrians, alert the driver and/ or automatically take actions to prevent vehicle-pedestrian collision. In some situations, a vehicle may not be able to detect a pedestrian due to blind spots. Such a vehicle could benefit from the sensor data from neighboring vehicles in making such safety critical decisions. We propose a trust model for ensuring shared data are valid and trustworthy for use in making safety critical decisions. Simulation results of the proposed trust model show promise
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MobileTrust: Secure Knowledge Integration in VANETs
Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
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