A revelation in today´s mobile is networks is SON (Self-Organizing technology) technology, which is seen as a playing pivotal role towards reducing the management costs of networks. In legacy networks, still many network elements and associated parameters are manually configured. The associated operations costs are significant. Specialized expertise must be maintained to tune these network parameters, and the existing manual process is time-consuming and potentially error-prone. In addition, this manual tuning process inherently results in comparatively long delays in updating values in response to the often rapidly changing network topologies and operating conditions, resulting in sub-optimal network performance. SON is capable of collecting information from the network, so as to perform self-configuration, self-optimization, self-healing and etc, so as to reduce the operation cost through less human involvement, and to optimize the service quality through robust and prompt network optimization. In this proposal, we aim to drive further cost savings in the way networks are managed today by amplifying further the coverage zone of SON within the network. We believe that key technologies such as network sharing and Coordinated Multipoint (CoMP) can benefit from SON technology solutions. We will consider a complex context-aware heterogeneous network that is slowly becoming a 5G reality, and investigate the notion of SON CoMP and SO network sharing, as key technologies to reduce cost and energy per bit in legacy and future emerging mobile technologies.

Intelligent Transportation Systems (ITS) are vital for enhancing road safety, alleviating traffic congestion, and saving energy in transport. However, due to the complex and dynamic operating environments of ITS including fast-moving vehicles, fluctuating vehicular communications, and scarce computing resources, ITS face unprecedented challenges in meeting the stringent service requirements in terms of ultra-high reliability and low-latency, demanded by the emerging mission-critical applications (e.g., autonomous driving and real-time intelligent traffic control). To address these challenges, ASCENT aims to form an international, multidisciplinary, and inter-sectoral consortium with world-leading experts to create a novel Autonomous Vehicular Edge Computing and Networking system empowered by advanced Artificial Intelligence (AI) technologies towards achieving reliable and efficient ITS. Specifically, ASCENT will pioneer research and innovations (R&I) on ground-breaking technologies including: 1) a novel and scalable system architecture that enables agile and reliable ITS service provisioning; 2) an original distributed AI framework fuelled by bespoke federated deep learning methods to offer pervasive intelligence; 3) innovative analytics tools to accurately predict dynamic network status including network traffic and channel quality; 4) autonomous and smart resource management schemes to support mission-critical ITS services. ASCENT will boost the R&I capability of partners in multiple disciplines and create a long-term cross-discipline and cross-sector knowledge-sharing platform with complementary expertise. The researchers involved will be trained through extensive R&I actions and well-planned networking activities to enrich their skill sets as well as enhance their career perspectives. The outcomes of ASCENT will significantly contribute to enhance the EU’s competitiveness and transforming our transportation systems into safer, smarter, and greener future ITS.

Environment disasters linked to climate change are predicted to cause huge economic loss of 200 billion Euros and endanger millions of people annually and globally by 2040. Internet-of-Things (IoT) and unmanned aerial vehicles (UAVs) are two promising technologies that can monitor environment changes and disasters to save lives and reduce disastrous consequences, but they both have inherent limitations: IoT networks often have high energy consumption and strong dependency on reliable communications, while UAVs cause significant concerns on reliability due to their high mobility and increased network complexity. The INITIATE project brings together an international, multidisciplinary and cross-sector consortium consisting of world-leading academic institutions and specialised industrial enterprises, to create a new generation of intelligent, sustainable and ultra-reliable aerial-terrestrial IoT networks that can equip humanity to fight against environment changes and disasters, through research and innovations (R&I) on advanced technologies in IoT, Artificial Intelligence, UAVs, wireless power transfer, software-defined networking, and network resource optimisation to tackle the associated challenges. INITIATE will boost the R&I capability of partners by performing dedicated research in these multiple disciplines and create a long-term cross-discipline and cross-sector knowledge sharing platform with competent and complementary expertise. The researchers involved will be trained through extensive R&I actions and well-planned networking activities at both Europe and global levels to enrich their skill sets as well as enhance their career perspectives. The INITIATE project will significantly contribute to the European competitiveness and leadership in the key sectors such as ICT, remote sensing, and environment monitoring by creating scientific breakthroughs, boosting R&I capabilities, and training people for improved employability.

The use of artificial intelligence (AI) in Edge computing is entering a new era based on the use of ubiquitous small and connected devices. Until now, Europe has not been doing well, as America sets the standards and most components are produced in Asia or America. This project believes doing better is realized by (1) Putting European values of self-organization, privacy by design and low use of energy in the core of the Edge Computing components that shape this new era, and delivering the technology needed to promote these values; (2) Focusing on pan European cooperation to ramp up the capabilities needed to deliver these new components at a scale that can make a real impact. Europe does not have huge IT leaders so cooperation from a very early phase is key. All partners in the project participate in delivering key parts of these new Edge Computing components; and (3) Demonstrating the use of these components in key European industrial areas. Clear and early examples are needed to un-lock corporate and external funding to deliver on the promise of this very exciting project. The DAIS project will research and deliver distributed artificial intelligent systems. It will not research new algorithms, as such, but solves the problems of running existing algorithms on these vastly distributed edge devices that are designed based on the above three European core values. The research and innovation activities are organized around eight complementary and mutually supportive supply chains. Five of these focus on delivering the hardware and software that is needed to run industrial-grade AI on different types of networking topologies. Three of the supply chains demonstrate how known AI challenges, from different functional areas, are met by this pan European effort. The DAIS project consists of 48 parties from 11 different countries. The DAIS project fosters cooperation between large and leading industrial players from different domains.

R-PODID (Reliable Powerdown for Industrial Drives) aims to develop an automated, cloudless, short-term fault-prediction for electric drives, power modules, and power devices, that can be integrated into power converters. Thereby, electrical and mechanical faults of machines and of the power converters driving them will become predictable within a limited prediction horizon of 12-24h. This will enable a power-saving shutdown of a larger number of production machines during idle times, because a looming failure during the next power-on cycle can be reliably foreseen. It will also enable reliable mitigation of dangerous faults in applications using modern power-devices like silicon-carbide (SiC) and III/V-semiconductor devices like gallium-nitride (GaN).