8,915 research outputs found
Privacy-Preserving Vehicle Assignment for Mobility-on-Demand Systems
Urban transportation is being transformed by mobility-on-demand (MoD)
systems. One of the goals of MoD systems is to provide personalized
transportation services to passengers. This process is facilitated by a
centralized operator that coordinates the assignment of vehicles to individual
passengers, based on location data. However, current approaches assume that
accurate positioning information for passengers and vehicles is readily
available. This assumption raises privacy concerns. In this work, we address
this issue by proposing a method that protects passengers' drop-off locations
(i.e., their travel destinations). Formally, we solve a batch assignment
problem that routes vehicles at obfuscated origin locations to passenger
locations (since origin locations correspond to previous drop-off locations),
such that the mean waiting time is minimized. Our main contributions are
two-fold. First, we formalize the notion of privacy for continuous
vehicle-to-passenger assignment in MoD systems, and integrate a privacy
mechanism that provides formal guarantees. Second, we present a scalable
algorithm that takes advantage of superfluous (idle) vehicles in the system,
combining multiple iterations of the Hungarian algorithm to allocate a
redundant number of vehicles to a single passenger. As a result, we are able to
reduce the performance deterioration induced by the privacy mechanism. We
evaluate our methods on a real, large-scale data set consisting of over 11
million taxi rides (specifying vehicle availability and passenger requests),
recorded over a month's duration, in the area of Manhattan, New York. Our work
demonstrates that privacy can be integrated into MoD systems without incurring
a significant loss of performance, and moreover, that this loss can be further
minimized at the cost of deploying additional (redundant) vehicles into the
fleet.Comment: 8 pages; Submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS), 201
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
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
Context-based Pseudonym Changing Scheme for Vehicular Adhoc Networks
Vehicular adhoc networks allow vehicles to share their information for safety
and traffic efficiency. However, sharing information may threaten the driver
privacy because it includes spatiotemporal information and is broadcast
publicly and periodically. In this paper, we propose a context-adaptive
pseudonym changing scheme which lets a vehicle decide autonomously when to
change its pseudonym and how long it should remain silent to ensure
unlinkability. This scheme adapts dynamically based on the density of the
surrounding traffic and the user privacy preferences. We employ a multi-target
tracking algorithm to measure privacy in terms of traceability in realistic
vehicle traces. We use Monte Carlo analysis to estimate the quality of service
(QoS) of a forward collision warning application when vehicles apply this
scheme. According to the experimental results, the proposed scheme provides a
better compromise between traceability and QoS than a random silent period
scheme.Comment: Extended version of a previous paper "K. Emara, W. Woerndl, and J.
Schlichter, "Poster: Context-Adaptive User-Centric Privacy Scheme for VANET,"
in Proceedings of the 11th EAI International Conference on Security and
Privacy in Communication Networks, SecureComm'15. Dallas, TX, USA: Springer,
June 2015.
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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
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