EMBERS will bring to market a back-end for smart city mobility that is developed by a European small enterprise based upon its smart parking and smart traffic management products that two municipalities in Portugal currently deploy. The Mobility Back-end as a Service (MBaaS) replaces such all-in-one systems, in which a municipality purchases the full set of components from a single vendor. Instead, the city manager can purchase best-of-breed devices and apps developed by third parties, with the only constraint being that they interoperate with the back-end via a free, open, smart city mobility API. This domain-specific API lowers barriers to entry for app and device developers, making it easier for innovative SMEs to enter the market. Furthermore, the API is offered via a variety of generic interfaces, including oneM2M, ETSI M2M, OMA LWM2M, and FIWARE NGSI. EMBERS thus clears the way for developers and to municipalities that have adopted any one of these potential emerging machine-to-machine (M2M) communication standards. Beyond its primary goal of bringing the MBaaS to market, EMBERS will stimulate development of an entire ecosystem around the MBaaS smart city mobility API. Separating out the back-end from the other components will, however, require rigorous testing. EMBERS will experiment with the system on two testbeds that are part of the FIRE+ OneLab facility: the FUSECO Playground, for M2M communications, and FIT IoT-LAB, for wireless sensor devices. EMBERS will host a hackathon and an app challenge to bring in third party developers. The project will also include three demonstrators by third parties via an open call. These activities will contribute back to FIRE+ by demonstrating successful experimentation by SMEs developing close-to-market products. The project will also conduct real world pilots in two or more cities as a final step in bringing the MBaaS to market.

Service-based architecture in the 5G core network, disaggregated RAN, and network slicing are a few of the 5G network's innovative paradigms. There are however new requirements for 6G, such as the efficient use of integrated cloud resources, end-to-end network programmability, and the dynamic integration of multiple connectivity domains to realize intelligent connectivity across increasingly pervasive cloud environments. The 6G-Cloud project will research, develop, and validate key technologies to realize an artificial intelligence (AI)-native and cloud-friendly system architecture atop the cloud continuum. It will integrate cloud resources offered by multiple stakeholders and allow network functions from different 6G network segments to be composed flexibly and dynamically based on service needs in hybrid cloud environments. 6G-Cloud will address the following key topics: i) designing a holistic, AI-native service-oriented 6G system architecture atop a cloud continuum; ii) developing AI-driven cloud continuum and management frameworks and business interfaces for a multistakeholder environment; iii) defining an AI/machine learning (ML) framework for native-AI support in the 6G system; iv) supporting the 6G "network-of-networks" concept using service-oriented network design. 6G-Cloud will incorporate scalability, sustainability, resilience, and security requirements into system design. The concept will be validated by three well-defined proofs-of-concept and promoted through 6G architectural standardization work and open-source projects to reach maximum impact.

The large-scale rollout of 5G networks has started becoming a reality, with big vendors deploying 5G network equipment and MNOs being on the verge of its commercialization. However, in parallel to the deployment of such 5G high-performing network, there is an unprecedented urge to support solutions tailored to specific types of networks, capable of offering ubiquitous coverage with high data rate availability, densification and high capillarity of access points to enhance 5G system capacity. Affordable5G aims at creating a 5G network that will deliver a complete and affordable solution covering the needs of private and enterprise networks through technical innovation that span across all parts of 5G network, leveraging cell densification, RU/DU/CU split, hardware acceleration, edge computing and core network virtualization, seamlessly combined with the adoption of open source RAN, MEC and MANO solutions, for cloud-native, micro-service based deployments. To achieve its innovative and ambitious goal, the consortium brings together ten European SMEs, supported by MVNOs, system integrators and research institutes, grasping the opportunity to enhance their products, according to each company’s roadmap, while fostering collaboration among them. In this way, Affordable5G will offer a first-class opportunity to European SMEs to become frontrunners in the global 5G competition, facilitating them in their commercialization paths and strategies in niche market cases of neutral hosting, private networks and MVNOs with new entrant actors. The innovative solution will be evaluated and validated in two vertical pilots related to emergency communications and smart cities, which have been properly selected as being highly representative in terms of system performance, scalability, mobility patterns, slice types, deployment requirements and impact in the future 5G market.

ETHER aims to provide a holistic approach for integrated terrestrial-non-terrestrial networks targeting at 100% network coverage, 99.99999% service continuity and 99.99999% reliability, with 3 times higher energy efficiency and 95% Total Cost of Ownership reduction compared to current terrestrial only deployments. To achieve these goals, ETHER develops solutions for a Unified Radio Access Network (RAN) and for the energy-efficient, AI-enabled resource management across the terrestrial, aerial and space domains, while creating the business plans driving future investments in the area. To that end, ETHER introduces and combines a series of key technologies under a unique 3D multi-layered architectural proposition that brings together: i) a UE antenna design and implementation for direct handheld access in the integrated network, ii) a robust unified waveform, iii) energy-efficient seamless horizontal and vertical handover policies, iv) a zero-touch management and network orchestrator to self-adapt to rapidly evolving traffic conditions without human intervention, v) a flexible payload system to enable programmability in the aerial and space layers, vi) joint communication, compute and storage resource allocation solutions targeting at End-to-End network performance optimization leveraging efficient and novel predictive analytics schemes, and vii) energy-efficient semantics-aware information handling techniques combined with edge computing and caching for reduced latency across the distributed 3D compute and storage continuum.

MLSysOps will achieve substantial research contributions in the realm of AI-based system adaptation across the cloud-edge continuum by introducing advanced methods and tools to enable optimal system management and application deployment. MLSysOps will design, implement and evaluate a complete framework for autonomic end-to-end system management across the full cloud-edge continuum. MLSysOps will employ a hierarchical agent-based AI architecture to interface with the underlying resource management and application deployment/orchestration mechanisms of the continuum. Adaptivity will be achieved through continual ML model learning in conjunction with intelligent retraining concurrently to application execution, while openness and extensibility will be supported through explainable ML methods and an API for pluggable ML models. Flexible/efficient application execution on heterogeneous infrastructures and nodes will be enabled through innovative portable container-based technology. Energy efficiency, performance, low latency, efficient, resilient and trusted tier-less storage, cross-layer orchestration including resource-constrained devices, resilience to imperfections of physical networks, trust and security, are key elements of MLSysOps addressed using ML models. The framework architecture disassociates management from control and seamlessly interfaces with popular control frameworks for different layers of the continuum. The framework will be evaluated using research testbeds as well as two real-world application-specific testbeds in the domain of smart cities and smart agriculture, which will also be used to collect the system-level data necessary to train and validate the ML models, while realistic system simulators will be used to conduct scale-out experiments. The MLSysOps consortium is a balanced blend of academic/research and industry/SME partners, bringing together the necessary scientific and technological skills to ensure successful implementation and impact.