2,011 research outputs found
A survey on pseudonym changing strategies for Vehicular Ad-Hoc Networks
The initial phase of the deployment of Vehicular Ad-Hoc Networks (VANETs) has
begun and many research challenges still need to be addressed. Location privacy
continues to be in the top of these challenges. Indeed, both of academia and
industry agreed to apply the pseudonym changing approach as a solution to
protect the location privacy of VANETs'users. However, due to the pseudonyms
linking attack, a simple changing of pseudonym shown to be inefficient to
provide the required protection. For this reason, many pseudonym changing
strategies have been suggested to provide an effective pseudonym changing.
Unfortunately, the development of an effective pseudonym changing strategy for
VANETs is still an open issue. In this paper, we present a comprehensive survey
and classification of pseudonym changing strategies. We then discuss and
compare them with respect to some relevant criteria. Finally, we highlight some
current researches, and open issues and give some future directions
Stable Infrastructure-based Routing for Intelligent Transportation Systems
Intelligent Transportation Systems (ITSs) have been instrumental
in reshaping transportation towards safer roads, seamless
logistics, and digital business-oriented services under the umbrella of
smart city platforms. Undoubtedly, ITS applications will demand
stable routing protocols that not only focus on Inter-Vehicle Communications
but also on providing a fast, reliable and secure interface to
the infrastructure. In this paper, we propose a novel stable infrastructure-
based routing protocol for urban VANETs. It enables vehicles
proactively to maintain fresh routes towards Road-Side Units
(RSUs) while reactively discovering routes to nearby vehicles. It
builds routes from highly stable connected intersections using a selection
policy which uses a new intersection stability metric. Simulation
experiments performed with accurate mobility and propagation
models have confirmed the efficiency of the new protocol and its
adaptability to continuously changing network status in the urban
environment
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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
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
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