OpenContinuum addresses the coordination and support of the Cloud-Edge-IoT domain, with a specific thematic focus on the supply-side of the computing continuum landscape. An integrated, open ecosystem built around Open Source, Open Standards, and the effective blending of the two vibrant European communities of Cloud Computing and Internet of Things (IoT) is the key enabler for European prosperity in the Cloud-Edge-IoT domain and more widely in the data economy. Therefore the core ambition of OpenContinuum is fostering European strategic autonomy and interoperability through an open ecosystem for the computing continuum. Such an ecosystem will contain the research and innovation projects in the Cloud-Edge-IoT portfolio to be coordinated, the diverse community evolved from the current Cloud and IoT ones, with the addition of further actors, initiatives, and alliances of significance. The supply-side nature of OpenContinuum's agenda will orient the themes and focus of the project activities, but will not limit the scope of the community building. The active landscaping and engagement work of the project will be key to bring the Cloud and IoT communities together and to express all points of view with a common language and understanding. OpenContinuum will then provide strong guidance and support to European computing continuum actors to contribute to and lead open-source projects and standardisation efforts. Key project contributions to the relevant expected outcomes include: a baseline common open architecture for computing continuum research projects, reinforced collaboration between European public and private initiatives from Cloud to edge to IoT, and increased awareness on the importance of Open Source and standards for EU digital autonomy. These contributions, arising from project results, will evolve further towards larger-scale impacts and strategic objectives for European technological, economic, and societal advancement.

RobMoSys will coordinate the whole community’s best and consorted effort to build an open and sustainable, agile and multi-domain European robotics software ecosystem. RobMoSys envisions an integration approach built on-top-of, or rather around, the current code-centric robotic platforms, by means of the systematic application of model-driven methods and tools that explicitly focus on (system-of-) system integration. As proven in many other engineering domains, model-driven approaches are the most suitable approach to manage integration that is intended to be “all-inclusive” with respect to technologies and stakeholder groups. RobMoSys will enable the management of the interfaces between different roles (robotics expert, domain expert, component supplier, system integrator, installation and deployment, operation) and separated concerns in an efficient and systematic way by making the step change to a set of fully model-driven methods and tools for engineering robotics systems. RobMoSys will drive the non-competitive part of building the eco-system aiming at turning community involvement into active support for an ecosystem of professional quality and scope. It will provide, based on broad involvement via two Open Calls, important concretizations for many of the common robot functionalities (sensing, planning, control in the broad sense). It will fulfill two complementary missions: (1) establish a common methodology enabling a composition-oriented approach to address complexity in robotics and face the integration burden caused by type diversity, target diversity and platform diversity; (2) stimulate and boost an ecosystem of methodology-based toolchains that supports the interaction of separated roles. RobMoSys is designed for widest inclusion - from the very beginning and throughout the overall course of the project - of the expertise and body of knowledge of the robotics community and of related relevant technology and application domains (Tier-1 concept).

PDP4E is an innovation action that will provide software and system engineers with methods and software tools to systematically apply data protection principles in the projects they carry out, so that the products they create comply with the General Data Protection Regulation (GDPR), thus bringing the principles of Privacy and Data Protection by Design to practice. PDP4E will integrate privacy and data protection engineering functionalities into existent, mainstream software tools that are already in use by engineers, focusing on open-source tools that will be integrated in the Eclipse ecosystem, The approach will integrate methods proposed by the privacy engineering community (e.g. LINDDUN, ISO/IEC 27550 Privacy engineering), and the industry of software and system engineering tools (e.g. MUSE, PAPYRUS or OpenCert) using a model driven engineering approach. PDP4E will introduce privacy and data protection into software and system engineering disciplines (Risk Management, Requirements Engineering, Model-Driven Design, and Assurance), which drive the everyday activities of engineers. Results of PDP4E will be assessed by two demonstration pilots on industries where privacy and data protection are especially relevant, one on C-ITS applications and services (connected vehicle application domain) and one on big data on smart grid (smart grid application domain). PDP4E will promote its results in engineering communities, as Eclipse (community of software developers) or IPEN (community of stakeholders with an interest on privacy engineering). An open Alliance for Privacy and Data Protection Engineering is planned as a follow-up of the project, building on that community and the synergies among partners. PDP4E includes 8 partners and has a 36-month duration.

The main objective of SmartCLIDE is to propose a radically new smart cloud-native development environment, based on the coding-by-demonstration principle, that will support creators of cloud services in the discovery, creation, composition, testing and deployment of full-stack data-centred services and applications in the cloud. SmartCLIDE will provide high levels of abstraction at all stages (development, testing, deployment and run-time) as well as self-discovery of IaaS and SaaS Services. SmartCLIDE will provide several categories of abstractions: at development stage, SmartCLIDE will provide abstractions on data transformations or processing; at testing stage, mechanisms to visualize flow and status or artefacts to automatically test the expected behaviour; at deployment stage, abstractions of physical and virtual resources; or at run-time, mechanisms to monitor the performance and operation of the service. The cloud nature of the environment will enable collaboration between different stakeholders, and the self-discovery of IaaS and SaaS services and the high levels of abstraction will facilitate the composition and deployment of new services to non-technical staff (with no previous experience on programming or on the administration of systems and infrastructure). Equally, hiding the complexity of the infrastructure, and adding intelligence to this layer, will allow to select the most adequate infrastructure services in each moment. SmartCLIDE will allow SMEs and Public Administration to boost the adoption of Cloud and Big Data solutions, being validated at one solution oriented to Public Administration (Social Security System) and three different IoT and Big Data products of software development SMEs within the consortium.

In HYPER-AI, we work with smart virtual computing entities (nodes) that come from a variety of infrastructures that span all three of the so-called computing continuum's layers: the Cloud, the Edge, and IoT. It focuses on intensive data-processing applications that present the potential to improve their footprint when hyper-distributed in an optimized manner. In order to give targeted applications access to computational, storage, or network services, HYPER-AI implements the idea of computing swarms as autonomous, self-organized, and opportunistic networks of smart nodes. These networks may offer a diverse and heterogeneous set of resources processing, storage, data, communication) at all levels and have the ability to dynamically connect, interact, and cooperate. HYPER-AI proposes semantic representation concepts to enable heterogeneous resources’ abstraction in a homogeneous way, under a common annotation (computing node), across the whole range of network infrastructures. The main orchestration and control concept of HYPER-AI is inspired by autonomic systems (self-CHOP principles) which employ swarmed computing schemes. Its objective is to make smart multi-node (swarm) deployment scenario design, execution, and monitoring easier, through appropriate AIs for self-configuration (nodes assigned resources), self-healing (swarmed nodes lifecycle), self-optimizing (exploiting built-in situation awareness mechanisms) and self-protecting (intrusion detection, privacy, security, encryption and identity management) at application runtime. In order to support dynamic and data-driven application workflows, HYPER-AI suggests the flexible integration of resources at the edge, the core cloud, and along the big data processing and communication channel, enabling their energy, time and cost-efficient execution. Finally, distributed ledger concepts for security, privacy, and encryption as well as AI-based intrusion detection are also considered.