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New Radio (NR) Vehicle-to-Everything (V2X) Sidelink (SL), an integral part of the 5G NR standard, is expected to revolutionize the automotive and rail industries by enabling direct and low-latency exchange of critical information between traffic participants independently of cellular networks. However, this advancement depends primarily on efficient SL resource allocation. Mode 2(a) is a well-known method for this purpose, where each node autonomously selects resources. However, this method is prone to packet collisions due to the hidden-node problem. In this paper, we propose a cooperative scheduling method that could potentially address this issue. We describe an extension of Mode 2(a) that allows nodes to share resource allocation information at two hops. Initial simulation results show a promising improvement over Mode 2(a)., Comment: arXiv admin note: This version has been removed by arXiv administrators as the submitter did not have the right to agree to the license at the time of submission

Significant progress has been made towards deploying Vehicle-to-Everything (V2X) technology. Integrating V2X with 5G has enabled ultra-low latency and high-reliability V2X communications. However, while communication performance has enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure our future roads. Many V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. Yet, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper present a comprehensive survey and classification of ML-based MDSs. We analyze and discuss them from both security and ML perspectives. Then, we give some learned lessons and recommendations helping in developing, validating, and deploying ML-based MDSs. Finally, we highlight open research and standardization issues with some future directions.

Topological sorting is an important technique in numerous practical applications, such as information retrieval, recommender systems, optimization, etc. In this paper, we introduce a problem of generalized topological sorting with maximization of choice, that is, of choosing a subset of items of a predefined size that contains the maximum number of equally preferable options (items) with respect to a dominance relation. We formulate this problem in a very abstract form and prove that sorting by k-Pareto optimality yields a valid solution. Next, we show that the proposed theory can be useful in practice. We apply it during the selection step of genetic optimization and demonstrate that the resulting algorithm outperforms existing state-of-the-art approaches such as NSGA-II and NSGA-III. We also demonstrate that the provided general formulation allows discovering interesting relationships and applying the developed theory to different applications.

In recent years, a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully applied to observational real-world data to determine the direction of causal relationships. Yet in bivariate situations, causal discovery problems remain challenging. One class of such methods, that also allows tackling the bivariate case, is based on Additive Noise Models (ANMs). Unfortunately, one aspect of these methods has not received much attention until now: what is the impact of different noise levels on the ability of these methods to identify the direction of the causal relationship. This work aims to bridge this gap with the help of an empirical study. We test Regression with Subsequent Independence Test (RESIT) using an exhaustive range of models where the level of additive noise gradually changes from 1\% to 10000\% of the causes' noise level (the latter remains fixed). Additionally, the experiments in this work consider several different types of distributions as well as linear and non-linear models. The results of the experiments show that ANMs methods can fail to capture the true causal direction for some levels of noise., Comment: Presented at 10\`emes Journ\'ees Francophones sur les R\'eseaux Bay\'esiens et les Mod\`eles Graphiques Probabilistes (JFRB-2021), https://pageperso.lis-lab.fr/christophe.gonzales/research/jfrb2021/programme.php

While the adoption of connected vehicles is growing, security and privacy concerns are still the key barriers raised by society. These concerns mandate automakers and standardization groups to propose convenient solutions for privacy preservation. One of the main proposed solutions is the use of Pseudonym-Changing Strategies (PCSs). However, ETSI has recently published a technical report which highlights the absence of standardized and efficient PCSs [1]. This alarming situation mandates an innovative shift in the way that the privacy of end-users is protected during their journey. Software-Defined Networking (SDN) is emerging as a key 5G enabler to manage the network in a dynamic manner. SDN-enabled wireless networks are opening up new programmable and highly-flexible privacy-aware solutions. We exploit this paradigm to propose an innovative software-defined location privacy architecture for vehicular networks. The proposed architecture is context-aware, programmable, extensible, and able to encompass all existing and future pseudonym-changing strategies. To demonstrate the merit of our architecture, we consider a case study that involves four pseudonym-changing strategies, which we deploy over our architecture and compare with their static implementations. We also detail how the SDN controller dynamically switches between the strategies according to the context.

Cross-border access to a variety of data such as market information, strategic information, or customer-related information defines the daily business of many global companies, including financial institutions. These companies are obliged by law to keep a data processing legal for all offered services. They need to fulfill different security objectives specified by the legislation. Therefore, they control access to prevent unauthorized users from using data. Those security objectives, for example confidentiality or secrecy, are often defined in the eXtensible Access Control Markup Language that promotes interoperability between different systems. In this paper, we show the necessity of incorporating the requirements of legislation into access control. Based on the work flow in a banking scenario we describe a variety of available contextual information and their interrelations. Different from other access control systems our main focus is on law-compliant cross-border data access. By including legislation directly into access decisions, this lawfulness can be ensured. We also describe our information model to demonstrate how these policies can be implemented into an existing network and how the components and contextual information interrelate. Finally, we outline an event flow for a request made from a remote user exemplifying how such a system decides about access., Comment: IEEE Publication Format, https://sites.google.com/site/journalofcomputing/