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This paper has been presented at IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops This demonstration shows how the 5G-TRANSFORMER platform, and more specifically the vertical slicer, is capable of arbitrating vertical services. In this context, arbitration refers to handling the various services of a given vertical customer according to their SLA requirements, service priorities, and resource budget available to the vertical. In this demo, a low priority video service of the automotive vertical is terminated when a high-priority intersection collision avoidance service needs to be instantiated and there are not enough resources allowing all services to be run in parallel. All these services are deployed with the help of the 5G-TRANSFORMER platform in a multi-PoP scenario, with PoPs in Barcelona (Spain), Turin (Italy), and Pisa (Italy), featuring a high variety of transport and computing technologies. Work supported in part by EU Commission H2020 5G-TRANSFORMER project (Grant No. 761536), H2020 5Growth project (Grant No. 856709), Spanish MINECO grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195.

Purpose: Accelerated by the increasing digitalization, the connection of humans and machines has exponentially grown in recent years and the trend continues to gain momentum. Connected cars follow this course of connecting everyday objects to the internet and as contemporary vehicles are becoming more networked, services tailored around connectivity functions form a central value proposition of modern OEM business models. Yet, while connectivity features extend and improve a vehicle’s functional portfolio, the dependency on personal user data fosters the relevance of informational privacy in vehicles. This thesis aims at examining the moderating role of data privacy risks in the adoption process of connected car features. Building on Davis’ Technology Acceptance Model, an adapted acceptance framework which accounts for data privacy risks of connected car features is proposed. Methodology: In a split-test experiment, 440 participants were exposed to different levels of connected car related data privacy risks and subsequently responded to an acceptance survey. Interaction based regression modelling revealed a statistically significant and practically meaningful attenuating moderation effect of data privacy risks on the perceived usefulness and perceived ease of use of connected car features, which in turn influenced usage intention. Findings: The obtained results underscore the importance of data privacy for the adoption of connected car features and contribute to the existing research by showing the importance of combining data privacy risks of connected cars with classical TAM constructs. Objectivo: Acelerada pela digitalização, a ligação entre humanos e máquinas cresceu exponencialmente nos últimos anos e a tendência continua a ganhar ímpeto. Os automóveis em rede [AR] seguem essa tendência de conectar objetos do quotidiano à Internet e, à medida que os veículos contemporâneos se tornam progressivamente mais digitais, os serviços afetos às funções de conectividade representam uma proposta de valor central nos modelos de negócio OEM. Contudo, enquanto as soluções de conectividade ampliam e melhoram as características funcionais de um veículo, a dependência de dados pessoais do utilizador promove a importância da privacidade dos mesmos. Esta tese visa examinar a função moderadora dos riscos da privacidade de dados no processo de adoção de funcionalidades dos AR. Com base no Modelo de Aceitação da Tecnologia (TAM) de Davis, é proposto um modelo de aceitação adaptado que considera os riscos associados à privacidade de dados nos AR. Metodologia: Numa experiência de teste dividida, cerca de 440 participantes foram expostos a diferentes níveis de risco de privacidade de dados relacionado com AR tendo, posteriormente, sido requisitada a sua resposta a um questionário de aceitação. Regressão Linear Interativa revelou um efeito de moderação atenuante, estatisticamente significativo, da privacidade de dados na utilidade e facilidade de uso perceptível das funcionalidades dos AR, o que influencia a intenção de uso. Resultados: Os resultados obtidos salientam a importância da privacidade de dados na adoção de funcionalidades dos AR contribuindo para a pesquisa existente salientando a relevância da combinação dos riscos da privacidade de dados com construções clássicas da TAM.

In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle's future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities. This work was supported by the EU Commission through the H2020 5G-TRANSFORMER project (Grant No. 761536) and the H2020 5GROWTH project (Grant No. 856709), by the CARS Interdepartmental Lab at Politecnico di Torino, by the Spanish MINECO Grant TEC2017- 88373-R (5G-REFINE), and by the Generalitat de Catalunya Grant 2017 SGR 1195.

Performance evaluation is integral to the vast majority of research on Vehicle-to-Vehicle (V2V) technology enabled connected cars. To validate ideas and concepts, researchers have been continuously striving towards the higher accuracy of simulation-based performance evaluation. However, many state-of-the-art network simulators lack comprehensive physical (PHY) layer models. More often, simplified representations of vehicular channel characteristics are used to achieve a trade-off between accuracy and performance. Vehicular channel modeling is a highly complex task because of its unique properties, for example, higher carrier frequency, rapid fluctuations in vehicular channels due to moving scatterers, and propagation in horizontal plane instead of a vertical plane with diffraction and reflection. Efficiently incorporating vehicular channel details into a single network simulator is infeasible; instead, a chain of simulation tools are used together. In this paper, we proposed a two-stage simulation framework which combines several layers of simulation tools into two distinct stages. During the first stage, a Geometry-based vehicular propagation model is used to characterize received signal strength among transmitter-receiver pairs. For this purpose, metropolitan area-wide 2.5D building geometry data and vehicular mobility traces are employed to represent the real-world environment. Subsequently, the output from the first stage is collected and fed as an input to the network simulator. Through extensive simulation-based studies, we analyze the difference between the proposed framework and standard propagation models implemented in the network simulator and their impact on the network-level performance metrics such as packet loss rate (PLR), throughput, latency, and jitter.

Self-driving and connected vehicles, communicating with one another (V2V technology) and with the road infrastructure (V2I technology), are a subject of extensive research nowadays and are expected to revolutionize the automotive industry in the near future. The major goal of the authors' work is to design a microscopic traffic simulation model for such vehicles, including a robust protocol for exchanging information. The question arises as to whether such communication system may efficiently improve travel quality while reducing the risk of collisions. For the purpose of their research the authors created and developed a simulation software. Their tool visualizes traffic flow for custom but simplified road maps. The transport infrastructure includes multiple junctions, optionally equipped with traffic lights, and roads with varying number of travel lanes. Each vehicle is assigned a fixed route leading to a randomly chosen destination point. Any decisions made by autonomous cars (regarding acceleration or turning maneuvers) are preceded by communication stages (retrieving necessary data, negotiations). In the paper we present fundamental concepts, assumptions and design of our model and simulation software, the authors also discuss potential issues relevant to their approach. As for the future work, the authors plan to implement their model in a large-scale agent-based traffic simulation software, Traffic Simulation Framework, so that further examination will be carried out for realistic road networks taken from the OpenStreetMap project. The authors also plan to apply machine learning techniques, so that self-driving vehicles, as well as traffic light controllers, will be able to learn how to develop the best strategy and by this way improve traffic safety and efficiency in atypical cases.

Significant strides are being made in research towards security and privacy concerns associated with connected cars. Being a relatively novel research domain, it is crucial to examine the state of the art to formulate future research strategies. The objective of the paper is to aggregate the research progress made in identifying security and privacy concerns in connected cars, and the concomitant solutions. Using systematic mapping study, we reviewed the state of the art to address the research objective. Results indicate that in the last couple of years, there has been an exponential increase in investigation into this research topic. Compromised vehicular communication has received more attention than any other concern, and the popularity has only increased in recent years. A secure system design and infrastructure is the most common solution for all the identified concerns in this paper, but securing communication is the most popular one. Real-world applicability of the devised solutions is debatable, as most researchers have preferred simulations to empirical evaluation. Overall, insecure system design or infrastructure warrants more attention from solution point of view. In this context, the principles of Prevention by Design and Privacy by Design can be among the most effective strategies.

Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is sound.

Content caching on the edge of 5G networks is an emerging and critical feature to support the thirst for content of future connected cars. Yet, the compactization of 5G cells, the finite edge storage capacity and the need for content availability while driving motivate the need to develop smart edge caching strategies adapted to the mobility characteristics of connected cars. In this paper, we propose a Mobility-Aware Probabilistic (MAP) scheme, which optimally caches content at edge nodes where connected vehicles mostly require it. Unlike blind popularity decisions, the probabilistic caching used by MAP considers vehicular trajectory predictions as well as content service time by edge nodes. We evaluate our approach on realistic mobility datasets and against popularity-based edge approaches. Our MAP edge caching scheme provides up to 40% enhanced content availability, 70% increased cache throughput, and 40% reduced backhaul overhead compared to popularity-based strategies., © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.