The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

In this paper we explore the problem of data coun-terfeiting in the context of data trading platforms for industrial data. We focus on the methods and mechanisms that allow data trading platforms to determine whether a dataset is original and/or authentic and whether it conflicts with the licence terms of existing data. We present the state of the art methods for data similarity suited for the various types of data and we present the functionalities and architecture of a data trading platform module that safeguards against data counterfeiting for newly introduced datasets within the platform.

Realizing desired properties “by construction” is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving.

The integration of connected and autonomous technologies in safety-critical and cyber-physical systems offers great potential in the vital application domains of transportation, manufacturing and aerospace. These technological advancements are necessary to meet the increasing demand for intelligent services, as they open doors to new business models by analysing and sharing the generated data. However, where this sharing of mix-critical data and broader connectivity brings opportunities, it simultaneously presents serious cybersecurity and safety risks due to the cyber-physical nature of these systems. Hence, delivering these intelligent services securely, safely, and reliably to its consumers is a complex engineering and design problem. One of the ways to approach this engineering problem is to consider both system functional and non-functional properties (safety, security, reliability) and systematically integrate them across system design and operational life cycle. The XANDAR project investigates this approach and aims to develop holistic software design methods and architectures for safety-critical and cyber-physical systems that guarantee functional and non-functional properties “byconstruction”. This paper focuses on the non-functional aspects of the project and discusses the preliminary work. by presenting the core cybersecurity principles and uses them as a baseline to propose a holistic cybersecurity engineering process. The tasks of the proposed cybersecurity engineering process are also map onto relevant clauses of ISO 21434. In future, proposed work will be integrated into the XANDAR software toolchain and validated for an avionics situation perception pilot assistance and automotive autonomous driving use cases.

The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, development of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems, which relies on Machine Learning (ML) and AI applications when combined with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are Advanced Driver-Assistance Systems (ADAS) that are prone to various safety/security vulnerabilities. The XANDAR project aims at developing a mature SW toolchain (from requirements analysis to the actual code integration on target including V&V) fulfilling the needs of industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel (real-time, safety-, and security)-by-Construction paradigm.

The goal of the SMART4ALL EU funded project is to build capacity amongst European stakeholders via the development of self-sustained, cross-border experiments that transfer knowledge and technology between academia and industry. It targets Customized Low Energy Computing (CLEC) in the Cyber-Physical (CPS) and the Internet of Things (IoT) domains. The vision of the project will be mainly realized through funded (via open calls) Pathfinder Application Experiments (PAEs) that will enable the transformation of academic knowledge into products.It is important to mention that SMART4ALL sets forward the concept of marketplace which is offered as a service (Marketplace-as-a-Servtce or MaaS) that acts as one-stopsmart-stop by offering tools, HPC services, and platforms to the PAE grantees. The purpose of this paper is to present the SMART4ALL High-Performance Computing (HPC) infrastructure and services realized according to the Hardware-as-a-Service paradigm. The SMART4A11 HPC infrastructure includes CPU, Memory, GPU, storage as well as FPGA resources connected with high-speed links.

It is increasingly regarded that data local caching in WSN can benefit the resource conservation and performance. A critical challenge is that cached data comprise only a small percentage of the data that a WSN node may require, thus leading to the replacement of data items. Consequently, replacement policy comprises critical factors in order to maximize performance benefits. Equally important in WSN is the interplay of such policies, application and network characteristics. Therefore, this paper aims to experimentally evaluate prominent replacement policies performance with respect to critical application and network parameters. An already presented cache model is developed and appropriately extended in the context of the prominent WSN TelosB platform. Valuable insights are provided concerning the behavior of considered policies in typical WSN scenarios and important trade-offs are revealed.

Wireless Sensor Networks (WSNs) have limited power capabilities, whereas they serve applications which usually require specific packets, i.e. High Priority Packets (HPP), to be delivered before a deadline. Hence, it is essential to reduce the energy consumption and to have real-time behavior. To achieve this goal we propose a hybrid technique which explores the benefits of data aggregation without data size reduction in combination with prioritized queues. The energy consumption is reduced by appending data from incoming packets with already buffered Low Priority Packets (LPP). The real-time behavior is achieved by directly forwarding the HPP to the next node. Our study explores the impact of the proposed hybrid technique in several all-to-one data flow scenarios with various traffic loads, wait time intervals and percentage of HPP. Our results show gain up to 23,3% in packet loss and 36,6% in energy consumption compared with the direct forwarding of packets.

The Wireless Sensor Networks (WSNs) have limited power and communication capabilities, combined with the requirement for long network lifetime. To increase it, methods to reduce energy consumption are highly required. To achieve this goal, we study a data aggregation technique without size reduction, i.e. data merge. It is a generic technique, since it is also usable in applications with heterogeneous data and requirements for high accuracy. This study presents the impact of the data merge technique on WSNs applications executed under various realistic data flow scenarios, traffic loads and wait time intervals. Our results show significant reductions in both packet loss and radio energy consumption.

We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using F(18) Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors.

A modular architecture for power constrained embedded devices which leverages an agent based middleware in order to impose intelligence over critical infrastructures which require real time actions is always desirable in the case of Wireless Sensor Networks. The main objective of such architecture is the integration of a Wireless Sensor Network with the internet. This objective indicates the need of Ipv6 ready devices as well as the utilization of newly emerged standards such as the 6lowpan which forms an abstraction layer between the Medium Access control and the IP layers.