7,627 research outputs found
A-VIP: Anonymous Verification and Inference of Positions in Vehicular Networks
MiniconferenceInternational audienceKnowledge of the location of vehicles and tracking of the routes they follow are a requirement for a number of applications, including e-tolling and liability attribution in case of accidents. However, public disclosure of the identity and position of drivers jeopardizes user privacy, and securing the tracking through asymmetric cryptography may have an exceedingly high computational cost. Additionally, there is currently no way an authority can verify the correctness of the position information provided by a potentially misbehaving car. In this paper, we address all of the issues above by introducing A-VIP, a lightweight framework for privacy preserving and tracking of vehicles. A-VIP leverages anonymous position beacons from vehicles, and the cooperation of nearby cars collecting and reporting the beacons they hear. Such information allows an authority to verify the locations announced by vehicles, or to infer the actual ones if needed. We assess the effectiveness of A-VIP through both realistic simulation and testbed implementation results, analyzing also its resilience to adversarial attacks
A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics
This research was funded by the Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia, under Tamayuz research grant number 2/710.Traffic congestion is still a challenge faced by most countries of the world. However, it can
be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog
computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of
these technologies to address specific problems faced in traffic management. Unfortunately, no such
framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management
exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and
efficient solution for the problem of traffic congestion. The proposed framework has four layers.
The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate
measurement of the traffic status of the streets, and forwards these data to the second layer. The
second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards
them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics
and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for
traffic management. We also discuss the functional model of the framework and the technologies
that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for
the efficient management of congestion at intersections, tweet-classification and image-processing
algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the
functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and
the tweet classification and image-processing algorithms for their accuracy.Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia 2/71
Flexible Authentication in Vehicular Ad hoc Networks
A Vehicular Ad-Hoc Network (VANET) is a form of Mobile ad-hoc network, to
provide communications among nearby vehicles and between vehicles and nearby
fixed roadside equipment. The key operation in VANETs is the broadcast of
messages. Consequently, the vehicles need to make sure that the information has
been sent by an authentic node in the network. VANETs present unique challenges
such as high node mobility, real-time constraints, scalability, gradual
deployment and privacy. No existent technique addresses all these requirements.
In particular, both inter-vehicle and vehicle-to-roadside wireless
communications present different characteristics that should be taken into
account when defining node authentication services. That is exactly what is
done in this paper, where the features of inter-vehicle and vehicle-to-roadside
communications are analyzed to propose differentiated services for node
authentication, according to privacy and efficiency needs
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
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