The emerging spring of Artificial Intelligence (AI) will enable innovative applications exploiting the myriad of connected sensors and appliances embedded in every corner of modern life. Currently, AI requires high computational resources only available in high-performance data centers; therefore, realizing an architecture capable of securely processing this unprecedented amount of remotely sensed and potentially sensitive data, as well as conveying timely responses to pervasive configurable actuators is a non-trivial endeavour, requiring the cooperation of multiple parties. To address these challenges, DECENTER aims to realise a robust Fog Computing platform, covering the whole Cloud-to-Things Continuum, that will provide AI application-aware orchestration and provisioning of resources. The project will enrich existing Cloud and IoT solutions with advanced capabilities to abstract features and process data closer to where it is produced. DECENTER will enable a collaborative environment in which multiple stakeholders (Cloud and IoT providers) can securely share and harmoniously manage resources, in dynamically created multi-cloud/edge, federated environments. Cross-border infrastructure federation will be realized via Blockchain-based Smart Contracts defining customized Service Level Agreements, used to commit the execution of verified workloads across multiple, potentially remote, administrative domains. Through such novelties, DECENTER will unlock the potential of innovative decentralised AI algorithms and models, by deploying them across multiple tiers of the infrastructure and federated clouds. The project will follow a lean implementation methodology and validate its concept with real-world pilots executed in urban, industrial and home environments. With its approach, DECENTER will target the emergence of innovative digital businesses, thus providing a competitive advantage to EU and Korean industry and fostering cross-border collaboration.

STARDUST serves as smart connector bringing together advanced European cities and citizens of Pamplona (ES), Tampere (FI) and Trento (IT) - with the associated follower cities of Derry (UK), Kozani (GR) and Litomerice (CZ). These six cities, collaborating with relevant industrial partners, including a variety of innovative local SME, and supported by academia and research centres will demonstrate three lighthouse cities, deploy intelligent integration measures, test and validate technical solutions and innovative business models, and deliver blueprints for replication throughout Europe and abroad. The objective of STARDUST project is to pave the way towards the transformation of the carbon supplied cities into Smart, high efficient, intelligent and citizen oriented cities, developing urban technical green solutions and innovative business models, integrating the domains of buildings, mobility and efficient energy through ICT, testing and validating these solutions, enabling their fast roll out in the market . The core idea of the STARDUST project is the demonstration of different “innovation islands” as urban incubators of technological, social, regulatory and market solutions which, once validated, could contribute to this objective of transformation of our cities towards Smart Cities. The integrated approach of STARDUST is based in the combination of technological solutions with human being reflected in joint decision making, economic constraints, citizen’s governance, etc. The STARDUST Smart City concept has been designed to enhance the integration of all these aspects to define a new Urban Metabolism.

Collecting and analysing large amounts of data in the Cloud-to-Edge computing continuum raises novel challenges. Processing all this data centrally in cloud data centres is not feasible anymore as transferring large amounts of data to the cloud is time-consuming, expensive, degrade performance and may raise security concerns. Therefore, novel distributed computing paradigms, such as edge and fog computing emerged to support processing data closer to its origin. However, such hyper-distributed systems require fundamentally new methods. To overcome the limitation of current centralised application management approaches, Swarmhestrate will develop a completely novel decentralised application-level orchestrator, based on the notion of self-organised interdependent Swarms. Application microservices are managed in a dynamic Orchestration Space by decentralised Orchestration Agents, governed by distributed intelligence that provides matchmaking between application requirements and resources, and supports the dynamic self-organisation of Swarms. Knowledge and trust, essential for the operation of the Orchestration Space, will be managed through blockchain-based trusted solutions using methods of Self-Sovereign Identities (SSI) and Distributed Identifiers (DID). End-to-end security of the overall system will be assured by utilising state-of-the-art cryptographic algorithms and privacy preserving data analytics. Due to the imminent complexity of the decentralised system, novel simulation approaches will be developed to test and optimise system behaviour (e.g., energy efficiency) in the early stages of development. Additionally, the simulator will be further extended into a digital twin running in parallel to the physical system and improving its behaviour with predictive feedback. The Swarmchestrate concept will be prototyped on four real-life demonstrators from the areas of flood prevention, parking space management, video analytics and a digital twin of natural habitat.

A seamless interaction with the public administration (PA) is crucial to make the daily activities of companies and citizens more effective and efficient, saving time and money in the management of administrative processes. In particular, online public services have an enormous potential for reducing the administrative burden of companies and citizens, as well as for creating saving opportunities for the PA. This potential is however far from being fully exploited. Online services made available by the PA typically rely on standardized processes, copied from their offline counterparts and designed only from the public sector organizations’ own perspective. This results in online services that fail to adapt to the specific needs of citizens and companies. With SIMPATICO, we address the issues above by proposing a novel approach for the delivery of personalized online services that, combining emerging technologies for language processing and machine learning with the wisdom of the crowd, makes interactions with the PA easier, more efficient and more effective. SIMPATICO combines top-down knowledge of the PA with bottom-up contributions coming from the community. These contributions can be of different types, ranging from the qualified expertise of civil servants and professionals to problems and doubts raised by citizens and companies that find online services difficult to use. Our approach is able to take into account both explicit information sources coming from citizens, professionals and civil servants, and implicit ones, extracted from user logs and past user interactions. SIMPATICO’s ‘learning by doing’ approach will use this information and match it with user profiles to continuously adapt and improve interactions with the public services. All the collected information on public services and procedures will be made available within Citizenpedia, a collective knowledge database released as a new public domain resource.

The importance of biodiversity (BD) and healthy ecosystems and the services they deliver has increasingly been acknowledged. Policy initiatives, such as the EU BD Strategies 2020 and 2030, IPBES, IPCC and CBD were launched to address BD, natural capital and related values. Research has created a knowledge base to better understand nature-human interactions that are at the base of ecosystem services (ES) delivery. The key aim of related actions is to provide robust information that can be harnessed to support protection, restoration and sustainable as well as climate-neutral use of ecosystems in the EU by 2030. MAES has provided the conceptual, methodological and data base for comprehensive assessments on different spatial scales, including the EU-wide assessment (2020) and assessments in EU member states. Knowledge and data for different ecosystem types (including protected and marine areas) are increasingly available. The next step is to integrate the different MAES components (ecosystem mapping, condition, ES, accounting) and to enable the uptake of ES in decision making. Key challenges include the proof of BD-ecosystem condition-ES relationships and to link them to EU policies. The consortium brings together experts from all EU member states, associated countries and EU overseas regions with stakeholders from various public and private sectors. The expertise in the consortium includes leading experts (ecologists, economists, social scientists) on ES science, ecosystem accounting and on science-policy-business interfaces from related actions (OpenNESS, ESMERALDA, MAIA, MAES, ESP, IPBES, BD Partnership). Therefore, the project will successfully address the call's challenges and provide applicable tools and models together with guidance how to use them. The project will deliver real cases for an evidence-, ES-based and harmonised decision making across Europe, enabling transformative change to halt BD decline and to secure essential ES-sustainable supply and use.