Edge computing offers many technical advantages, e.g., reduced latency, secure decentralized processing and storage, scalability at lower complexity, versatility to adapt the changes in resources and applications, and increased reliability. Edge computing can dramatically boost services and applications by supporting artificial intelligence (AI) natively, instead of relying on the cloud. Edge computing supporting AI is the only technology that will enable many of the long-awaited game changers: Industry4.0 and smart manufacturing, 5G, IoT, self-driving vehicles, remote robotics for healthcare, and machine vision among others. The BRAINE project’s overall aim is to boost the development of the Edge framework focusing on energy efficient hardware and AI empowered software, capable of processing Big Data at the Edge, supporting security, data privacy, and sovereignty. The approach is to build a seamless Edge MicroDataCenter interlinked with AI enabled network interface cards. BRAINE employs a visionary utilization method for edge resources management and network-edge workload distribution. Predicting resource availability and workload demand, identifying trends, and taking proactive actions are all aspects of these novel methods. The impact of BRAINE encompasses advances in the European video distribution ecosystem, improving data processing at the network edge, and proving integrated AI for applications. These will lead to unprecedented savings in performance and energy efficiency: 2x performance/Watt and 50% energy savings; 71% latency reduction for an acceleration centric EMDC; 80% space and maintenance reduction, 99.999% fault tolerance with level 5 autonomy (autonomous driving, robotics, mission-critical system); and significantly faster infrastructure installation and deployment. BRAINE boosts EU’s position in the intelligent edge computing field and enables growth across many sectors, e.g., manufacturing, smart healthcare, surveillance, satellite navigation.

ICT4CART, totally aligned with the work programme, is providing an ICT infrastructure to enable the transition towards road transport automation. To meet this high level objective ICT4CART is bringing together, adapting and improving technological advances from different industries, mainly telecom, automotive and IT. It adopts a hybrid communication approach where all the major wireless technologies, i.e. cellular, ITS G5 and LTE-V, are integrated under a flexible “sliced” network architecture. This architecture will ensure performance and resilience for different groups of applications according to the needs of higher levels of automation (L3 & L4). On top of that, a distributed IT environment for data aggregation and analytics will be implemented. This offers seamless integration and exchange of data and services between all the different actors, allowing 3rd parties to develop, deliver and provide innovative services, thus creating new business opportunities. Cyber-security and data privacy aspects will be duly considered throughout the whole ICT infrastructure. In addition, novel accurate localisation services, exploiting the cellular network and information from other sources, such as on-board sensors, especially in complex areas (e.g. urban), will be addressed. Standardisation and interoperability are of high interest within ICT4CART in order to ensure the impact of the project towards enabling the transition to higher levels of automation. In this context issues related to the frequency spectrum will be investigated, while through the organisation of relevant workshops the engagement of policy makers and public authorities is ensured. To achieve its objectives ICT4CART, instead of working in generic solutions with questionable impact, it builds on four specific high-value use cases (urban and highway) which will be demonstrated and validated under real-life conditions at the test sites in Austria, Germany, Italy and across the Italian-Austrian border.

Requirements engineering is a key activity in ICT projects: What are current user needs and what requirements satisfy them? How much effort would a requirement cost and in which release should it be delivered? Which requirements can be reused from similar projects? Are there hidden dependencies or inconsistencies? What trade-offs are acceptable for users and other stakeholders? A satisfactory, efficient answer to these questions is essential for the success for nowadays software projects. OPENREQ leverages modern recommender algorithms, semantic technologies and data-mining to provide intelligent, proactive support for stakeholders survey alternatives and make individual or group decisions. OPENREQ focuses on complex, community-driven ICT projects with various dependencies and stakeholders as in the Telecom, Transportation, and Cross-Platform-Software domain covered in our trials. We will develop, evaluate and disseminate a fully integrated open-source requirements management platform and a set of connectors with the following decision-making capabilities: Requirements Intelligence: monitors the actual software usage, collects stakeholders’ and users’ feedback (e.g. from social media), aggregates and visualizes this information as predictive analytics. Stakeholder’s Personal Recommender: implements advanced recommendation and machine-learning algorithms to assist requirements work, improve a requirement’s quality, estimate its properties or predict relevant stakeholders. Group Decision Support: enables the stakeholders’ participation, the resolution of preference conflicts, and the identification of consensus, e.g. during release planning. Dependency Management: semi-automatically identifies requirements dependencies, supports requirements reasoning and reuse of requirements knowledge. With the OPENREQ Interfaces, these capabilities will be integrated into stakeholders’ workflows and tools including requirements tools, issues trackers and collaboration tools.