The next generation internet initiative

Digital transformation is pushing all market sectors to level up their digital capabilities to better serve customers and improve the user experience. The European Commission launched in 2016 the Next Generation Internet (NGI) initiative as part of the DSM strategy. NGI includes a number of different – but always interrelated – emerging technologies in the following focus areas: artificial intelligence and autonomous machines, blockchains and distributed ledgers, big data, Internet of Things, 5G, cybersecurity and privacy technologies, cloud and edge computing, and open data. As for cooperation in the field of Information and Communications Technology, Europe and the United States should seek a joint framework to expand efforts in new emerging technologies, while preserving common principles around a comprehensive EU–US digital economy dialogue. The NGI Initiative is an important opportunity to radically rethink the way the Internet works today, and more human-focused narratives are needed more than ever

Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

An Open Source Cyberinfrastructure for Collecting, Processing, Storing and Accessing High Temporal Resolution Residential Water Use Data

Collecting and managing high temporal resolution residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. We developed an open-source, modular, generalized architecture called Cyberinfrastructure for Intelligent Water Supply (CIWS) to automate the process from data collection to analysis and presentation of high temporal residential water use data. A prototype implementation was built using existing open-source technologies, including smart meters, databases, and services. Two case studies were selected to test functionalities of CIWS, including push and pull data models within single family and multi-unit residential contexts, respectively. CIWS was tested for scalability and performance within our design constraints and proved to be effective within both case studies. All CIWS elements and the case study data described are freely available for re-use

Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges

In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the digital and physical world. Many human activities now not only operate in geographical (physical) space but also in cyberspace. Such changes have triggered a paradigm shift in geographic information science (GIScience), as cyberspace brings new perspectives for the roles played by spatial and temporal dimensions, e.g., the dilemma of placelessness and possible timelessness. As a discipline at the brink of even bigger changes made possible by machine learning and artificial intelligence, this paper highlights the challenges and opportunities associated with geographical space in relation to cyberspace, with a particular focus on data analytics and visualization, including extended AI capabilities and virtual reality representations. Consequently, we encourage the creation of synergies between the processing and analysis of geographical and cyber data to improve sustainability and solve complex problems with geospatial applications and other digital advancements in urban and environmental sciences

Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms

Destination Earth: Survey on “Digital Twins” technologies and activities, in the Green Deal area

Digital Twins have been around for decades, especially in industrial processes. However, with the recent advent of transformative digital technologies (i.e. IoT, AI, ML, Big Data analytics, and ubiquitous connectivity) Digital Twins are changing most of the society sectors, providing the most advance pattern to make the physical and the digital worlds interact. Naturally, this is also true for the scientific sector, and in particular those disciplines that are engaged in understanding and addressing the Global Change effects. Thanks to the Digital Twins growing development, for the first time, it is possible to envision a digital replica of important natural and social phenomena and processes, trying to anticipate their behaviour. There exist diverse definitions of Digital Twins, reflecting the diverse concerns of the industrial, scientific, and standardization sectors (in particular IEEE and ISO/IEC), which have been working on their description and realization. The main interaction features characterizing a Digital Twin are: - Interoperability; - Information Model; - Data Exchange; - Administration; - Synchronization; - Push mode (Publish Subscribe). According the scientific research, there is still the need to address the following challenges to push Digital Twins implementation and effective use: - Unify data and model standards; - Share data and models; - Innovate on services; - Establish forums. In industry, Digital Twins are well used in “vertical” sectors/application areas, including: manufacturing, energy, smart cities, farming, building, healthcare. For the applied scientific and research areas, this preliminary study recognized several areas.JRC.B.6-Digital Econom

Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants

On Big Data and Hydroinformatics:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

AbstractBig data is an increasingly hot concept in the past five years in the area of computer science, e-commence, and bioinformatics, because more and more data has been collected by the internet, remote sensing network, wearable devices and the Internet of Things. The big data technology provides techniques and analytical tools to handle large datasets, so that creative ideas and new values can be extracted from them. However, the hydroinformatics research community are not so familiar with big data. This paper provides readers who are embracing the data-rich era with a timely review on big data and its relevant technology, and then points out the relevance with hydroinformatics in three aspects

Explotación de nuevas oportunidades científicas de los sistemas de posicionamiento global por satélite (GNSS) desde una perspectiva intensiva en datos

[EN] With the current GNSS infrastructure development plans, over 120 GNSS satellites (including European Galileo satellites)will provide, already this decade, continuous data, in several frequencies, without interruption and on a permanent basis.This global and permanent GNSS infrastructure constitutes a major opportunity for GNSS science applications. In themeantime, recent advances in technology have contributed "de-facto" to the deployment of a large GNSS receiver arraybased on Internet of Things (IoT), affordable smart devices easy to find in everybody’s pockets. These devices – evolvingfast at each new generation – feature an increasing number of capabilities and sensors able to collect a variety ofmeasurements, improving GNSS performance. Among these capabilities, Galileo dual band smartphones receivers andAndroid’s support for raw GNSS data recording represent major steps forward for Positioning, Navigation and Timing (PNT)data processing improvements. Information gathering from these devices, commonly referred as crowdsourcing, opensthe door to new data-intensive analysis techniques in many science domains. At this point, collaboration between variousresearch groups is essential to harness the potential hidden behind the large volumes of data generated by thiscyberinfrastructure. Cloud Computing technologies extend traditional computational boundaries, enabling execution ofprocessing components close to the data. This paradigm shift offers seamless execution of interactive algorithms andanalytics, skipping lengthy downloads and setups. The resulting scenario, defined by a GNSS Big Data repository with colocatedprocessing capabilities, sets an excellent basis for the application of Artificial Intelligence / Machine Learning (ML)technologies in the context of GNSS. This unique opportunity for science has been recognized by the European SpaceAgency (ESA) with the creation of the Navigation Scientific Office, which leverages on GNSS infrastructure to deliverinnovative solutions across multiple scientific domains.[ES] Con los planes actuales de desarrollo de la infraestructura GNSS, más de 120 satélites GNSS (incluidos los satélites europeos Galileo) proporcionarán, ya en esta década, datos continuos, en varias frecuencias, sin interrupciones y de forma permanente. Esta infraestructura GNSS global y permanente constituye una gran oportunidad para las aplicaciones científicas de GNSS. Mientras tanto, avances recientes han contribuido al despliegue de una red GNSS paralela basada en la Internet de las Cosas (IoT), asequibles dispositivos inteligentes fáciles de encontrar en todos los bolsillos. Estos dispositivos, que evolucionan rápidamente con cada nueva generación, acumulan un número creciente de funcionalidades y sensores capaces de recopilar una gran variedad de mediciones. Entre estas funcionalidades, los receptores de teléfonos inteligentes de banda dual Galileo y el soporte Android para la grabación de datos GNSS sin procesar representan pasos especialmente relevantes. La recopilación de información mediante estos dispositivos, comúnmente conocida como crowdsourcing, abre la puerta a nuevas técnicas de análisis de datos en múltiples dominios científicos. Llegados a este punto, la colaboración entre diversos grupos de investigación resulta esencial para aprovechar el potencial que se esconde en los grandes volúmenes de datos generados por esta ciberinfraestructura. Las tecnologías de Cloud Computing extienden los límites computacionales tradicionales permitiendo la ejecución de componentes de procesamiento cerca de los datos. Este cambio de paradigma ofrece una rápida ejecución de algoritmos y análisis interactivos, omitiendo largas descargas y configuraciones. El escenario resultante, definido por un repositorio GNSS Big Data con capacidades de procesamiento acopladas, establece una base excelente para la aplicación de tecnologías de Inteligencia Artificial / Aprendizaje Automático (ML). Esta oportunidad única para la ciencia ha sido reconocida por la Agencia Espacial Europea (ESA) con la creación de la Oficina Científica de Navegación, que aprovecha la infraestructura GNSS para ofrecer soluciones innovadoras en múltiples dominios científicos.This work was supported by the European Space Agency as part of Research and Development Programmes under Science and Navigation Directorates. The authors would like to thank the GNSS Science Advisory Committee and ESA Navigation Support Office for their support and suggestions. We also thank our Industrial partners, involved in science use cases assessment and implementation. Thanks also to the Science and Operations technical IT Unit at ESAC supporting the deployment of the GSSC Thematic Exploitation Platform. We would like to thank all data collection providers, with special thanks to IGS, ILRS, CDDIS, BKG and IGN for their sustained and remarkable support making possible the creation of the GSSC Repository at the core of this work.Navarro, V.; Ventura-Traveset, J. (2021). A data-intensive approach to exploit new GNSS science opportunities. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 43-53. https://doi.org/10.4995/CiGeo2021.2021.12740OCS435