Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

From Spatial Data Infrastructures to Data Spaces—A Technological Perspective on the Evolution of European SDIs

The availability of timely, accessible and well documented data plays a central role in the process of digital transformation in our societies and businesses. Considering this, the European Commission has established an ambitious agenda that aims to leverage on the favourable technological and political context and build a society that is empowered by data-driven innovation. Within this context, geospatial data remains critically important for many businesses and public services. The process of establishing Spatial Data Infrastructures (SDIs) in response to the legal provisions of the European Union INSPIRE Directive has a long history. While INSPIRE focuses mainly on 'unlocking' data from the public sector, there is need to address emerging technological trends, and consider the role of other actors such as the private sector and citizen science initiatives. The objective of this paper, given those bounding conditions is twofold. Firstly, we position SDI-related developments in Europe within the broader context of the current political and technological scenery. In doing so, we pay particular attention to relevant technological developments and emerging trends that we see as enablers for the evolution of European SDIs. Secondly, we propose a high level concept of a pan-European (geo)data space with a 10-year horizon in mind. We do this by considering today's technology while trying to adopt an evolutionary approach with developments that are incremental to contemporary SDIs

Internet of Things in Geospatial Analytics

Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things emerged as a holistic proposal to enable an ecosystem of varied, heterogeneous networked objects and devices to speak and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth jointly form interrelated infrastructures for addressing modern pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth.Comment: Book chapter at the Manual of Digital Earth Book, ISDE, September 2019, Editors: Huadong Guo, Michael F. Goodchild and Alessandro Annoni, (Publisher: Springer, Singapore

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

Emerging approaches for data-driven innovation in Europe: Sandbox experiments on the governance of data and technology

Europe’s digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Emerging approaches for data-driven innovation in Europe

Europe's digital transformation of the economy and society is one of the priorities of the current Commission and is framed by the European strategy for data. This strategy aims at creating a single market for data through the establishment of a common European data space, based in turn on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces

Digital earth:yesterday, today, and tomorrow

The concept of Digital Earth (DE) was formalized by Al Gore in 1998. At that time the technologies needed for its implementation were in an embryonic stage and the concept was quite visionary. Since then digital technologies have progressed significantly and their speed and pervasiveness have generated and are still causing the digital transformation of our society. This creates new opportunities and challenges for the realization of DE. ‘What is DE today?’, ‘What could DE be in the future?’, and ‘What is needed to make DE a reality?’. To answer these questions it is necessary to examine DE considering all the technological, scientific, social, and economic aspects, but also bearing in mind the principles that inspired its formulation. By understanding the lessons learned from the past, it becomes possible to identify the remaining scientific and technological challenges, and the actions needed to achieve the ultimate goal of a ‘Digital Earth for all’. This article reviews the evolution of the DE vision and its multiple definitions, illustrates what has been achieved so far, explains the impact of digital transformation, illustrates the new vision, and concludes with possible future scenarios and recommended actions to facilitate full DE implementation.</p

EVA: Emergency Vehicle Allocation

Emergency medicine plays a critical role in the development of a community, where the goal is to provide medical assistance in the shortest possible time. Consequently, the systems that support emergency operations need to be robust, efficient, and effective when managing the limited resources at their disposal. To achieve this, operators analyse historical data in search of patterns present in past occurrencesthat could help predict future call volume. This is a time consuming and very complex task that could be solved by the usage of machine learning solutions, which have been performed appropriately in the context of time series forecasting. Only after the future demands are known, the optimization of the distribution of available assets can be done, for the purpose of supporting high-density zones. The current works aim to propose an integrated system capable of supporting decision-making emergency operations in a real-time environment by allocating a set of available units within a service area based on hourly call volume predictions. The suggested system architecture employs a microservices approach along with event-based communications to enable real-time interactions between every component. This dissertation focuses on call volume forecasting and optimizing allocation components. A combination of traditional time series and deep learning models was used to model historical data from Virginal Beach emergency calls between the years 2010 and 2018, combined with several other features such as weather-related information. Deep learning solutions offered better error metrics, with WaveNet having an MAE value of 0.04. Regarding optimizing emergency vehicle location, the proposed solution is based on a Linear Programming problem to minimize the number of vehicles in each station, with a neighbour mechanism, entitled EVALP-NM, to add a buffer to stations near a high-density zone. This solution was also compared against a Genetic Algorithm that performed significantly worse in terms of execution time and outcomes. The performance of EVALP-NM was tested against simulations with different settings like the number of zones, stations, and ambulances.A medicina de emergência desempenha um papel fundamental no desenvolvimento da Sociedade, onde o objetivo é prestar assistência médica no menor tempo possível. Consequentemente, os sistemas que apoiam as operações de emergência precisam de ser robustos, eficientes e eficazes na gestão dos recursos limitados. Para isso, são analisados dados históricos no intuito de encontrar padrões em ocorrências passadas que possam ajudar a prever o volume futuro de chamadas. Esta é uma tarefa demorada e muito complexa que poderia ser resolvida com o uso de soluções de Machine Learning, que têm funcionado adequadamente no contexto da previsão de séries temporais. Só depois de conhecida a demanda futura poderá ser feita a otimização da distribuição dos recursos disponíveis, com o objetivo de suportar zonas de elevada densidade populacional. O presente trabalho tem como objetivo propor um sistema integrado capaz de apoiar a tomada de decisão em operações de emergência num ambiente de tempo real, atribuindo um conjunto de unidades disponíveis dentro de uma área de serviço com base em previsões volume de chamadas a cada hora. A arquitetura de sistema sugerida emprega uma abordagem de microserviços juntamente com comunicações baseadas em eventos para permitir interações em tempo real entre os componentes. Esta dissertação centra se nos componentes de previsão do volume de chamadas e otimização da atribuição. Foram usados modelos de séries temporais tradicionais e Deep Learning para modelar dados históricos de chamadas de emergência de Virginal Beach entre os anos de 2010 e 2018, combinadas com informações relacionadas ao clima. As soluções de Deep Learning ofereceram melhores métricas de erro, com WaveNet a ter um valor MAE de 0,04. No que diz respeito à otimização da localização dos veículos de emergência, a solução proposta baseia-se num problema de Programação Linear para minimizar o número de veículos em cada estação, com um mecanismo de vizinho, denominado EVALP-NM, para adicionar unidades adicionais às estações próximas de uma zona de alta densidade de chamadas. Esta solução foi comparada com um algoritmo genético que teve um desempenho significativamente pior em termos de tempo de execução e resultados. O desempenho do EVALP-NM foi testado em simulações com configurações diferentes, como número de zonas, estações e ambulâncias

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works