5,011 research outputs found
Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough
Inter-vehicle communication (IVC) systems disclose rich location information about vehicles. State-of-the-art security architectures are aware of the problem and provide privacy enhancing mechanisms, notably pseudonymous authentication. However, the granularity and the amount of location information IVC protocols divulge, enable an adversary that eavesdrops all traffic throughout an area, to reconstruct long traces of the whereabouts of the majority of vehicles within the same area. Our analysis in this paper confirms the existence of this kind of threat. As a result, it is questionable if strong location privacy is achievable in IVC systems against a powerful adversary.\u
Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration
Vehicular fog computing (VFC) has been envisioned as a promising paradigm for
enabling a variety of emerging intelligent transportation systems (ITS).
However, due to inevitable as well as non-negligible issues in wireless
communication, including transmission latency and packet loss, it is still
challenging in implementing safety-critical applications, such as real-time
collision warning in vehicular networks. In this paper, we present a vehicular
fog computing architecture, aiming at supporting effective and real-time
collision warning by offloading computation and communication overheads to
distributed fog nodes. With the system architecture, we further propose a
trajectory calibration based collision warning (TCCW) algorithm along with
tailored communication protocols. Specifically, an application-layer
vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable
distribution with real-world field testing data. Then, a packet loss detection
mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories
based on received vehicle status including GPS coordinates, velocity,
acceleration, heading direction, as well as the estimation of communication
delay and the detection of packet loss. For performance evaluation, we build
the simulation model and implement conventional solutions including cloud-based
warning and fog-based warning without calibration for comparison. Real-vehicle
trajectories are extracted as the input, and the simulation results demonstrate
that the effectiveness of TCCW in terms of the highest precision and recall in
a wide range of scenarios
Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication
In this paper, a minimalist, completely distributed freeway traffic
information system is introduced. It involves an autonomous, vehicle-based jam
front detection, the information transmission via inter-vehicle communication,
and the forecast of the spatial position of jam fronts by reconstructing the
spatiotemporal traffic situation based on the transmitted information. The
whole system is simulated with an integrated traffic simulator, that is based
on a realistic microscopic traffic model for longitudinal movements and lane
changes. The function of its communication module has been explicitly validated
by comparing the simulation results with analytical calculations. By means of
simulations, we show that the algorithms for a congestion-front recognition,
message transmission, and processing predict reliably the existence and
position of jam fronts for vehicle equipment rates as low as 3%. A reliable
mode of operation already for small market penetrations is crucial for the
successful introduction of inter-vehicle communication. The short-term
prediction of jam fronts is not only useful for the driver, but is essential
for enhancing road safety and road capacity by intelligent adaptive cruise
control systems.Comment: Published in the Proceedings of the Annual Meeting of the
Transportation Research Board 200
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