43 research outputs found
Agile Calibration Process of Full-Stack Simulation Frameworks for V2X Communications
Computer simulations and real-world car trials are essential to investigate
the performance of Vehicle-to-Everything (V2X) networks. However, simulations
are imperfect models of the physical reality and can be trusted only when they
indicate agreement with the real-world. On the other hand, trials lack
reproducibility and are subject to uncertainties and errors. In this paper, we
will illustrate a case study where the interrelationship between trials,
simulation, and the reality-of-interest is presented. Results are then compared
in a holistic fashion. Our study will describe the procedure followed to
macroscopically calibrate a full-stack network simulator to conduct
high-fidelity full-stack computer simulations.Comment: To appear in IEEE VNC 2017, Torino, I
Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs
Future Connected and Automated Vehicles (CAVs) will be supervised by
cloud-based systems overseeing the overall security and orchestrating traffic
flows. Such systems rely on data collected from CAVs across the whole city
operational area. This paper develops a Fog Computing-based infrastructure for
future Intelligent Transportation Systems (ITSs) enabling an agile and reliable
off-load of CAV data. Since CAVs are expected to generate large quantities of
data, it is not feasible to assume data off-loading to be completed while a CAV
is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be
in the range of an RSU only for a limited amount of time, necessitating data
reconciliation across different RSUs, if traditional approaches to data
off-load were to be used. To this end, this paper proposes an agile Fog
Computing infrastructure, which interconnects all the RSUs so that the data
reconciliation is solved efficiently as a by-product of deploying the Random
Linear Network Coding (RLNC) technique. Our numerical results confirm the
feasibility of our solution and show its effectiveness when operated in a
large-scale urban testbed.Comment: To appear in IEEE VTC-Spring 201
A City-Scale ITS-G5 Network for Next-Generation Intelligent Transportation Systems: Design Insights and Challenges
As we move towards autonomous vehicles, a reliable Vehicle-to-Everything
(V2X) communication framework becomes of paramount importance. In this paper we
present the development and the performance evaluation of a real-world
vehicular networking testbed. Our testbed, deployed in the heart of the City of
Bristol, UK, is able to exchange sensor data in a V2X manner. We will describe
the testbed architecture and its operational modes. Then, we will provide some
insight pertaining the firmware operating on the network devices. The system
performance has been evaluated under a series of large-scale field trials,
which have proven how our solution represents a low-cost high-quality framework
for V2X communications. Our system managed to achieve high packet delivery
ratios under different scenarios (urban, rural, highway) and for different
locations around the city. We have also identified the instability of the
packet transmission rate while using single-core devices, and we present some
future directions that will address that.Comment: Accepted for publication to AdHoc-Now 201
Location Anomalies Detection for Connected and Autonomous Vehicles
Future Connected and Automated Vehicles (CAV), and more generally ITS, will
form a highly interconnected system. Such a paradigm is referred to as the
Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to
orchestrate traffic flows in cities. For optimal decision making and
supervision, traffic centres will have access to suitably anonymized CAV
mobility information. Safe and secure operations will then be contingent on
early detection of anomalies. In this paper, a novel unsupervised learning
model based on deep autoencoder is proposed to detect the self-reported
location anomaly in CAVs, using vehicle locations and the Received Signal
Strength Indicator (RSSI) as features. Quantitative experiments on simulation
datasets show that the proposed approach is effective and robust in detecting
self-reported location anomalies.Comment: Accepted to IEEE CAVS 201
Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs
Future Connected and Automated Vehicles (CAVs) will be supervised by
cloud-based systems overseeing the overall security and orchestrating traffic
flows. Such systems rely on data collected from CAVs across the whole city
operational area. This paper develops a Fog Computing-based infrastructure for
future Intelligent Transportation Systems (ITSs) enabling an agile and reliable
off-load of CAV data. Since CAVs are expected to generate large quantities of
data, it is not feasible to assume data off-loading to be completed while a CAV
is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be
in the range of an RSU only for a limited amount of time, necessitating data
reconciliation across different RSUs, if traditional approaches to data
off-load were to be used. To this end, this paper proposes an agile Fog
Computing infrastructure, which interconnects all the RSUs so that the data
reconciliation is solved efficiently as a by-product of deploying the Random
Linear Network Coding (RLNC) technique. Our numerical results confirm the
feasibility of our solution and show its effectiveness when operated in a
large-scale urban testbed.Comment: To appear in IEEE VTC-Spring 201
Location Anomalies Detection for Connected and Autonomous Vehicles
Future Connected and Automated Vehicles (CAV), and more generally ITS, will
form a highly interconnected system. Such a paradigm is referred to as the
Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to
orchestrate traffic flows in cities. For optimal decision making and
supervision, traffic centres will have access to suitably anonymized CAV
mobility information. Safe and secure operations will then be contingent on
early detection of anomalies. In this paper, a novel unsupervised learning
model based on deep autoencoder is proposed to detect the self-reported
location anomaly in CAVs, using vehicle locations and the Received Signal
Strength Indicator (RSSI) as features. Quantitative experiments on simulation
datasets show that the proposed approach is effective and robust in detecting
self-reported location anomalies.Comment: Accepted to IEEE CAVS 201
A comprehensive survey of V2X cybersecurity mechanisms and future research paths
Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the ”Ministerio de Asuntos Económicos y Transformacion Digital” and the European Union-NextGenerationEU in the frameworks of the ”Plan de Recuperación, Transformación y Resiliencia” and of the ”Mecanismo de Recuperación y Resiliencia” under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version
A software architecture for electro-mobility services: a milestone for sustainable remote vehicle capabilities
To face the tough competition, changing markets and technologies in automotive industry,
automakers have to be highly innovative. In the previous decades, innovations were
electronics and IT-driven, which increased exponentially the complexity of vehicle’s internal
network. Furthermore, the growing expectations and preferences of customers oblige these
manufacturers to adapt their business models and to also propose mobility-based services.
One other hand, there is also an increasing pressure from regulators to significantly reduce
the environmental footprint in transportation and mobility, down to zero in the foreseeable
future.
This dissertation investigates an architecture for communication and data exchange
within a complex and heterogeneous ecosystem. This communication takes place between
various third-party entities on one side, and between these entities and the infrastructure
on the other. The proposed solution reduces considerably the complexity of vehicle
communication and within the parties involved in the ODX life cycle. In such an
heterogeneous environment, a particular attention is paid to the protection of confidential
and private data. Confidential data here refers to the OEM’s know-how which is enclosed
in vehicle projects. The data delivered by a car during a vehicle communication session
might contain private data from customers. Our solution ensures that every entity of this
ecosystem has access only to data it has the right to. We designed our solution to be
non-technological-coupling so that it can be implemented in any platform to benefit from
the best environment suited for each task. We also proposed a data model for vehicle
projects, which improves query time during a vehicle diagnostic session. The scalability and
the backwards compatibility were also taken into account during the design phase of our
solution.
We proposed the necessary algorithms and the workflow to perform an efficient vehicle
diagnostic with considerably lower latency and substantially better complexity time and
space than current solutions. To prove the practicality of our design, we presented a
prototypical implementation of our design. Then, we analyzed the results of a series of tests
we performed on several vehicle models and projects. We also evaluated the prototype
against quality attributes in software engineering