278 research outputs found
Reducing Message Collisions in Sensing-based Semi-Persistent Scheduling (SPS) by Using Reselection Lookaheads in Cellular V2X
In the C-V2X sidelink Mode 4 communication, the sensing-based semi-persistent
scheduling (SPS) implements a message collision avoidance algorithm to cope
with the undesirable effects of wireless channel congestion. Still, the current
standard mechanism produces high number of packet collisions, which may hinder
the high-reliability communications required in future C-V2X applications such
as autonomous driving. In this paper, we show that by drastically reducing the
uncertainties in the choice of the resource to use for SPS, we can
significantly reduce the message collisions in the C-V2X sidelink Mode 4.
Specifically, we propose the use of the "lookahead," which contains the next
starting resource location in the time-frequency plane. By exchanging the
lookahead information piggybacked on the periodic safety message, vehicular
user equipments (UEs) can eliminate most message collisions arising from the
ignorance of other UEs' internal decisions. Although the proposed scheme would
require the inclusion of the lookahead in the control part of the packet, the
benefit may outweigh the bandwidth cost, considering the stringent reliability
requirement in future C-V2X applications.Comment: Submitted to MDPI Sensor
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
AI and IoT Meet Mobile Machines: Towards a Smart Working Site
Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)
Design of an adaptive congestion control protocol for reliable vehicle safety communication
[no abstract
Open Platforms for Connected Vehicles
L'abstract è presente nell'allegato / the abstract is in the attachmen
AI and IoT Meet Mobile Machines
Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)
CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks
The main goal of the H2020-CARAMEL project is to address the cybersecurity gaps introduced by the new technological domains adopted by modern vehicles applying, among others, advanced Artificial Intelligence and Machine Learning techniques. As a result, CARAMEL enhances the protection against threats related to automated driving, smart charging of Electric Vehicles, and communication among vehicles or between vehicles and the roadside infrastructure. This work focuses on the latter and presents the CARAMEL architecture aiming at assessing the integrity of the information transmitted by vehicles, as well as at improving the security and privacy of communication for connected and autonomous driving. The proposed architecture includes: (1) multi-radio access technology capabilities, with simultaneous 802.11p and LTE-Uu support, enabled by the connectivity infrastructure; (2) a MEC platform, where, among others, algorithms for detecting attacks are implemented; (3) an intelligent On-Board Unit with anti-hacking features inside the vehicle; (4) a Public Key Infrastructure that validates in real-time the integrity of vehicle’s data transmissions. As an indicative application, the interaction between the entities of the CARAMEL architecture is showcased in case of a GPS spoofing attack scenario. Adopted attack detection techniques exploit robust in-vehicle and cooperative approaches that do not rely on encrypted GPS signals, but only on measurements available in the CARAMEL architecture.This work was supported by the European Union’s H2020 research and innovation programme under the CARAMEL
project (Grant agreement No. 833611). The work of Christian Vitale, Christos Laoudias and Georgios Ellinas was also
supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS
CoE) and from the Republic of Cyprus through the Directorate General for European Programmes, Coordination,
and Development. The work of Jordi Casademont and Pouria Sayyad Khodashenas was also supported by FEDER
and Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya
through projects Fem IoT and SGR 2017-00376 and by the ERDFPeer ReviewedPostprint (author's final draft
On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications
Recently, the millimeter wave (mmWave) band has been investigated as a means
to support the foreseen extreme data rate demands of emerging automotive
applications, which go beyond the capabilities of existing technologies for
vehicular communications. However, this potential is hindered by the severe
isotropic path loss and the harsh propagation of high-frequency channels.
Moreover, mmWave signals are typically directional, to benefit from beamforming
gain, and require frequent realignment of the beams to maintain connectivity.
These limitations are particularly challenging when considering
vehicle-to-vehicle (V2V) transmissions, because of the highly mobile nature of
the vehicular scenarios, and pose new challenges for proper vehicular
communication design. In this paper, we conduct simulations to compare the
performance of IEEE 802.11p and the mmWave technology to support V2V
networking, aiming at providing insights on how both technologies can
complement each other to meet the requirements of future automotive services.
The results show that mmWave-based strategies support ultra-high transmission
speeds, and IEEE 802.11p systems have the ability to guarantee reliable and
robust communications.Comment: 7 pages, 5 figures, 2 tables, accepted to the IEEE Connected and
Automated Vehicles Symposium (CAVS
- …