Wireless networks have become indispensable to citizens, enterprises and vertical industries, e.g., transport (including autonomous vehicles and drones), logistics, utilities and manufacturing, because wireless connectivity is essential to the digital transformation of industrial and business processes and customer experiences. As a result, wireless networks are facing increasingly diverse service requirements, which indicate that existing reactive network management will be insufficient, while intelligent and proactive control of service-centric networks becomes essential. Future intelligent wireless networks rely on several multidisciplinary breakthroughs: (i) Artificial Intelligence (AI) algorithms that can accurately predict spatial-temporal patterns of service demand and thereby drive proactive optimisation of wireless networks, (ii) reconfigurable radio access networks (RAN) and wireless environments, and (iii) automated wireless service provisioning with reduced cost, improved performance and greater reliability. Current research to automate the optimisation of service-centric wireless networks using data-driven AI is facing many open challenges that need to be urgently addressed. In this project, we will take advantage of growing data availability and advanced data science technologies, as well as AI algorithms and techniques to deliver the above identified multidisciplinary breakthroughs, thereby enabling reliable automated wireless service provisioning.

Whilst traffic demand is increasing exponentially, network operators’ revenue remains flat. There is an urgent for data driven 4G/5G networks. In this project, we exploit heterogeneous big data analytics to optimize both the deployment and operations of wireless networks. We design protocols that enable future Data Aware Wireless Networks (DAWN) for enabling a new age of Internet of Everything (IoE). The proposal has been developed to address the following open issues in data driven flexible systems: • How to characterize user mobility and wireless data traffic patterns • How to infer user Quality-of-Experience (QoE) from combining data sets • How to use data analytics to assist cell planning • How to use data driven techniques to optimise the network using Self-Organising-Network (SON) algorithms • How to optimally cache data to accelerate and optimise data storage and transmission. The research objectives of the DAWN4IoE project are as follows: • Develop appropriate spatial-temporal structured filters to combine different data sets and infer both human location/mobility and digital data demand patterns. • Develop appropriate machine-learning techniques for unstructured natural language processing (NLP) to understand consumer experience for different service categories. • Design algorithms to integrate the new data analytics techniques with current state-of-the-art deployment techniques to assist HetNet planning, performance prediction, and deployment • Design mechanisms to integrate structured and unstructured data analytics to drive SON algorithms for radio resource management and smart antenna elements. • Design algorithms to optimally cache data leveraging on mobile edge computing (MEC). Achieving the above objectives will provide crucial inputs for 5G/B5G data-driven flexible wireless network design and both increase network capacity by 50% and decrease operation costs by 20-30% (compared with non-data driven networks).

As today “to out compute is to out compete”, the position of Europe in all industrial sectors and societal challenges is highly depending on the technological progress in computing. However, although computing has reached an unparalleled progress, it still remains a research topic as new challenges impose its transformative nature and adaptation, among others the evolution towards Cyber Physical Systems, the proliferation of devices and the big data they produce, the reduction of energy footpring, and the abstraction of infrastructure complexity. To that respect, PHANTOM wishes to deliver an economically and energetically sustainable solution for next generation computing systems, through a cross-layer design comprising reconfigurable multi-core and heterogeneous hardware platforms managed by a hardware-agnostic software platform that hides the complexity from the programmer and offers multi-dimensional optimization. Specifically, PHANTOM is structured in three layers. First, parallel programming and productivity tools are provided including application-driven APIs for programming and annotations, a parallelization toolset for maintaining intrinsically the code parallelization and model based testing techniques for early parallel program verification. Then, multi-dimensional optimization is addressed through an adaptive and multi-objective scheduler, deciding on where to execute each application component, which is supported by runtime monitoring/data analytics and security implementations. Last, low power, heterogeneous hardware platforms are built together with system software for enabling their management as a service. PHANTOM brings multi-disciplinary expertise through an ecosystem of academia, industries and a strong number of SMEs. The outcome will be validated in three use cases (automotive, telecoms, surveillance), in order to prove a cross-market approach, while documentation for developers will be delivered and reusability readiness evaluation will be run.

Urban areas host 70% of the EU population, and it is estimated that this will increase up to 84% by 2050, exacerbating the EU cities’ current daunting problems such as air quality and greenhouse gas (GHG) emissions. In 2019, the transport sector was accountable for 27% of total EU GHG emissions, of which road transport accounted for approximately 72%. Many cities are looking to promote public and nonmotorised forms of transport to provide access to safe, affordable, accessible, and sustainable transport systems for all citizens. Many cities in Europe and worldwide are witnessing an increasing interest in shared and multi-modal mobility services using modes such as carsharing, bikes, ride-hailing, e-scooters facilitated by the emergence of intermediary mobility-on-demand platforms. Mobility services accessible ‘on demand’ can be achieved by an integrated Multimodal Intelligent Transport System (M-ITS) incorporating both motorised and non-motorised transport as well as private and public systems through the concept of Mobility as a Service (MaaS) Sustainable mobility alternatives such as a fully integrated and seamless multimodal mobility system that can be made widely available with the communication and computation advancements. RE-ROUTE an integRated intElligent multi-modal tRanspOrt infrastrUcTurE: distributed localised decision-making at the network edge forms an international, interdisciplinary, and inter-sectoral network of institutions with complementary skills, working on a joint research and knowledge transfer programme to improve the multi-modal Mobility-as-a- Service transport integration through localised Edge-based real-time data sharing, decentralised decision making, and localised network that places the processing at the proximity of data, contribute to evidence-based policy and physical infrastructure development and developing a unique skill set for the participating partners and improving their career prospects in the emerging ITS job market.

Water management requires massive, low-cost monitoring means coping with differentiated and evolving requirements. However, the majority of multifunctional water sensors only supports predefined goals hindering interoperability, with a high cost, impeding large scale deployments. Addressing this, PROTEUS aims at offering x10 reduction in both size and unit function cost compared to state of the art. To this end, an increased number of functions will be integrated at a reduced cost and PROTEUS will deliver a reconfigurable microfluidic-and nano-enabled sensor platform for cognitive water quality monitoring. Innovative embedded software will provide reconfigurability of the sensing board to support several differentiated applicative goals while cognitive capabilities will manage evolving requirements during exploitation. Energy autonomy will be made by harvesting water flow energy. In addition, low cost of additional sensing components will enable redundancy increasing life span of the systems. The main challenge relates to the heterogeneous integration into a monolithic, microfluidic sensing chip of carbon-nanotubes-based resistive chemical sensors, of MEMS physical and rheological resistive sensors and of a multifunctional adaptive deep-submicron CMOS system on chip. Upstream, high level system design addressing industrial use cases, manufacturability and cost-effectiveness, packaging, energy budget and interfaces between building blocks, will enable consistency and efficiency of the whole approach. Downstream, system validation will be carried out at different levels: benchmarking, reliability assessment to guarantee service time, model deployments and field testing. The consortium brings together renowned actors along the whole value chain, including system integration and end users. This will contribute to post-project exploitation prepared by ensuring appropriate inclusion of business requirements within the system design.