Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in efficient person detection, making them an essential technological tool to first responders. Multi-UAV systems extend these benefits by using coordinated strategies to cover large areas efficiently, reducing overall mission response time and enhancing its success. Despite these advantages, challenges remain in ensuring the safety, security, and dependability of (mutli-)UAV missions. Issues such as navigation risks, potential cyber threats, and hardware-/software-related reliability issues can impact the mission results. Additionally, UAVs are highly constrained devices with limited battery capacity, requiring the use of lightweight technologies. In this paper, we present part of the results of the SESAME project, an EU multi-partner project that aims to develop safe and secure multi-robot Systems. In particular, we present some of the developed SESAME Executable Digital Dependability Identities (EDDI) technologies based on Markov models, statistical distance measures, and other advanced approaches for enhancing safety, security and dependability of the UAV platform and underlying models. These EDDI technologies are seamlessly integrated using the ConSerts framework in a multi-UAV platform and tested using search and rescue scenarios. The results demonstrate significant improvements in multi-UAV safety, with an availability rate of 91% and a search and rescue algorithmic accuracy of 99.8%. Additionally, the system achieves precise detection of spoofing attacks, using collaborative localization as a mitigation technique to guide the UAV to a safe landing, even in the absence of GPS signals,

Safety is a key factor in the development of critical systems, encompassing both conventional types, such as aircraft, and modern technologies, such as autonomous vehicles. Failures during their operation can be potentially far-reaching and impact people and the environment. To certify these systems and enable their employment, regulatory bodies require, among others, a safety case. However, the growing complexity of modern systems and iterative nature of development pose significant challenges to the traditional approaches for creating safety cases that are still used in practice. Furthermore, safety cases are often generated in an ad-hoc manner and remain disconnected from system models and related artefacts. Without these connections it is difficult to construct the proper infrastructure for producing and maintaining safety cases in a structured manner throughout the system lifecycle. This paper presents our innovative method, Andromeda, and its underpinning metamodel, which establish connections between safety cases, system models, safety assessment activities aligned with international safety standards, and argument patterns. Automation is applied across various stages of the production of argument structures that support safety assurance and certification activities. Andromeda is complemented by tool-support designed to facilitate its application, and we demonstrate our work through a case study from the aviation industry. • A metamodel linking systems/analyses/patterns to aid algorithmic argument synthesis. • Patterns elicited by commonalities in standards used to construct safety arguments. • Optimal SILs allocation automated via metaheuristics to support safety arguments.

Trust is a fundamental aspect in enabling self-adaptation of intelligent systems and in paving the way towards a smooth adoption of technological innovations in our societies. While Artificial Intelligence (AI) is capable to uplift the human contribution to our societies while protecting environmental resources, its ethical and technical trust dimensions bring significant challenges for a sustainable self-adaptive evolution in the domain of safety-critical systems. Inspired from the safety assurance case, in this paper we introduce the concept of trust assurance case together with the implementation of its ethical and technical principles directed towards assuring a trustworthy sustainable evolution of safety-critical AI-controlled systems.

Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers' needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autonomous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to reorganise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) - a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship.

Abstract The future will encompass heavily interconnected, distributed, heterogeneous and intelligent systems which are bound to have a significant economic and social impact. Cyber physical systems (CPS) such as autonomous cars, smart electric grid, implanted medical devices and smart manufacturing are some practical examples of these intelligent systems. However, due to the open and cooperative nature of CPS, assuring their dependability is a challenge. The DEIS project addresses this important and unsolved challenge by developing the concept of a digital dependability identity (DDI). A DDI contains all the information that uniquely describes the dependability characteristics of a CPS or CPS component. DDIs are synthesised at development time and are the basis for the (semi)automated integration of components into systems during development, as well as for the fully automated dynamic integration of systems into systems of systems in the field.