Stakeholders from the built environment (designers, contractors, owners, facility managers) often face problems related to data access and sharing, on one hand, and to the integration of high volumes of heterogeneous data from different knowledge domains, on the other hand. These limitations directly impact the effectiveness of data processing and data analysis and are a common issue in the field of Architecture, Engineering, and Construction (AEC). When considering the built environment, practitioners cannot rely only on building-level data; buildings evolve in a much larger ecosystem expanding upwards to the level and scale of a city and downwards to the level and scale of products and related components (e.g. installed heating, ventilating and air conditioning - HVAC components, wall types, window and door materials, floor finishing, domotica) and sensors (e.g. temperature sensors, air quality sensors, cameras, smart appliances sensors). This ecosystem involves an increasing number of services and applications. Even with access to standardized building information, multiple data sources have to be integrated for an organization to hold a considerable competitive advantage. A uniform approach is thus needed for modelling data and processes at any level of granularity, from the level of real-time sensor monitoring to the level of long-term urban planning. The multi-disciplinary goal of this research project is to deliver a spatio-temporal model for Big semantic and spatio-temporal Data extracted from a variety of sources. For the processing of data, an innovative lambda architecture will ease the definition of a Big Data architecture allowing a seamless real-time semantic and spatio-temporal qualitative analysis of the data mentioned above. The spatio-temporal issue is present both at the level of the data model itself and at the level of sensors and the context in which they perform data acquisition (component, building or urban level, indoor or outdoor). Our project addresses the following issues: a) delivering a novel architecture for optimizing big semantic and spatio-temporal data processing tasks; b) implementing distributed semantic and spatio-temporal data and process qualitative analysis and mining, at the service of stakeholders from the built environment; c) allowing real-time geospatial event processing, over a large number of high volume streams of sensor or third party data. Coupling the defined data model to the defined infrastructure allows capturing sensor data and integrating a business-related spatio-temporal reference. By merging building-internal and building-external knowledge, along with qualitative and quantitative analysis tools, we deliver upper-level business services (Big Data as a Service). Finally, we investigate and deliver a specialization of those services for the smart buildings and the smart cities domains. This project addresses cross-sector (building industry, geospatial management, process modelling, multi-level sensor analysis) and cross-border (urban administrations, national and local governments, European regulations) issues, our goal in this MRSEI funding request is to build a consortium of public and private partners for addressing these challenges in the domain of built environment life-cycle management. We have identified the "Big data PPP: research addressing main technology challenges of the data economy" (ICT-16-2017) call as most pertaining given our project's goals. ICT-16-2017 summarizes the exact key challenge in handling big semantic and spatio-temporal built environment data (GIS, BIM, and sensor data). Our project aims at extending results obtained in previous EU FP7 projects such as City Pulse, Ready4SmartCities, SO-PC-Pro, DURAAK, along with respecting the directives defined through EU FP6 INSPIRE.

The LEONIDAS action was submitted for the first time in the last MSCA-ITN 2017 call. According to the Evaluation Summary Report the overall score we obtained was of 94.2%. Not even one weakness was highlighted (only strengths were reported) and the scores for each evaluated part were as follows: Criterion 1–Excellence, Score: 4.70; Criterion 2–Impact, Score: 4.80; Criterion 3-Quality and Efficiency of the Implementation, Score: 4.60. In spite of this very positive evaluation, the project was not financed (threshold to go was 96.2%). Clearly, a financial support such as the MRSEI, devoted to improving the quality of the rebuttal of our proposal in 2018, may represent the key to obtain the grant. We aim at creating an European research ITN network for the training of young scientists in the research field of quantum photonics based on silicon devices. We will provide to the PhDs an overview over silicon devices from the fundamental issues of Si purification and growth, ending up at the pinnacle of the modern communication era: quantum computation and information processing. The use of a silicon platform, and the involvement of industrial members, are strategic assets of the ITN: the implementation of the research program will lead to new science and new applications of tremendous relevance in semiconductor physics, devices and applications. LEONIDAS will train 15 PhD in the field of Si-based nano-structures, photonics and electronics towards the implementation of quantum devices. The trainees will be exposed to ideas, methods and issues relevant to the largest world-wide semiconductor market, offering them a wide spectrum of choices in their careers in academia and in the private company sectors (e.g. photovoltaics, transistors, cameras, detectors, mobiles etc.). Nano-photonics for solid-state quantum information requires great challenges in the fabrication and understanding of efficient quantum emitters with tailored properties, in the development of new scientific equipment enabling advanced experiments and devices at the single/multiple quantum level. For widespread nano-photonics and quantum information applications, semiconductor devices need to be engineered with full freedom and easily integrated in existing platforms and technologies. A main requirement is the implementation of new devices in a silicon platform, with different architectures and tunable optical, electrical and spin properties. These requirements are met by the control over sample purity, high quality of ion implantation and by all the widespread expertise in Si-based micro- and nano-structures. Despite a growing demand of innovative applications in this strongly multi-disciplinary research area, besides mutual collaborations on sub-fields, there are no research networks covering the entire topic. Our aim is to bring together European groups with a recognized expertise in growth, microscopy, spectroscopy, theory and device fabrication, so to cover the full chain of research in the field of quantum information processing, from basic materials science to practical devices. The input to LEONIDAS of non-academic members is crucial for the achievement of the proposed objectives, as well as for the enhancement of the training environment. In this way we will create a research and training action aiming at studying the ion implantation methods, deeply understanding the involved physics targeting well defined fundamental and technological goals. The LEONIDAS proposal timely matches all the 6 key-activities suggested by the “Quantum Manifesto” proposal (QUROPE) ensuring that the trained researchers will be uniquely well-placed to contribute to the development of new quantum devices in silicon. Our action will strongly enhance existing collaborations among consortium Partners, now covering only specific aspects of the whole chain of research.

The IoT will contain a huge number of devices and objects that have very low or nonexistent processing and communication resources, coupled to a small number of high-power devices. The weakest devices, which are most ubiquitous, will not be able to authenticate themselves using cryptographic methods. Other important tasks in the IoT will be to verify if an object is authentic, or to identify an object. Our plan is to address these issues using Physical Unclonable Functions (PUFs). PUFs, and especially Quantum Readout PUFs, are ideally suited to the IoT setting because they allow for the authentication and identification of physical objects without requiring any crypto or storage of secret information. Furthermore, we foresee that back-end systems will not be able to provide security and privacy via cryptographic primitives due to the sheer number of IoT devices. Our plan is to address these problems using privacy-preserving database structures and algorithms with good scaling behaviour. Approximate Nearest Neighbour (ANN) search algorithms, which have remarkably good scaling behaviour, have recently become highly efficient, but do not yet have the right security properties and have not yet been applied to PUF data. Summarised in a nutshell, the project aims to improve the theory and practice of technologies such as PUFs and ANN search in the context of generic IoT authentication and identification scenarios.