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

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

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

FOSS4G 2016 Proceedings: Academic Program - selected papers and posters

This Conference Proceedings is a collection of selected papers and posters submitted to the Academic Program of the International Conference for Free and Open Source Software for Geospatial (FOSS4G 2016), 24th to 26th August 2016 in Bonn, Germany. Like in previous FOSS4G conferences on national and international level the academic papers and posters cover an extensive wide range of topics reflecting the contribution of the academia to this field by the development of open source software components, in the design of open standards, in the proliferation of web-based solutions, in the dissemination of the open principles important in science and education, or in the collection and the hosting of freely available geo-data

A spatio-temporal modelling and analysis of digital sensor data for underground mine health and safety

A Research Report submitted to the Faculty of Science, University of the Witwatersrand, in partial fulfilment of the requirements of the degree of Master of Science 2017Health and safety of employees within their work environment is critical. In the mining industry and especially in underground mines, monitoring and management of health and safety of employees is particularly important Most underground mines today are not fully mechanized, except for coal mines. The industry thus still relies on and employs human personnel. Monitoring and managing these mines and hence personnel health and safety as they undertake their trade is therefore a necessity. Implementation of technology, especially in digital sensor systems and real-time spatial analysis systems, provides a means by which health and safety risk factors can be monitored and information gathered to facilitate determination of prevailing risks or prediction of such risks. Technology therefore can be used to make better decisions and implement specialized emergency response to avert or reduce the extent of injuries, casualties and damages in an underground mine. This research project looks into determination of prominent risk factors in an underground mine, determination of parameters for modeling of such risk factors and the implementation of ESRI’s ArcGIS platform for the retrieval and analysis of streaming sensor data about this parameter from an underground mine. A proof of concept (POC) system is developed that analyses streaming digital sensor data and determines the status of the underground mine environment. The results from this analysis are displayed in a dashboard application for a control room environment. The results and achievements of this research project, especially from a dashboard system perspective, show the possibilities of an integrated GIS-based solution for real-time data processing and determination of the prevailing conditions in an underground mine. This solution also opens up a wide pool of possibilities through which systems integration and its benefits can be achieved, especially in underground mines and focusing on health and safety, as previously silo systems can be integrated at data levels, enabling data sharing, analysis, predictions and making of informed decisions.MT201

WEB MAPPING ARCHITECTURES BASED ON OPEN SPECIFICATIONS AND FREE AND OPEN SOURCE SOFTWARE IN THE WATER DOMAIN

The availability of water-related data and information across different geographical and jurisdictional scales is of critical importance for the conservation and management of water resources in the 21st century. Today information assets are often found fragmented across multiple agencies that use incompatible data formats and procedures for data collection, storage, maintenance, analysis, and distribution. The growing adoption of Web mapping systems in the water domain is reducing the gap between data availability and its practical use and accessibility. Nevertheless, more attention must be given to the design and development of these systems to achieve high levels of interoperability and usability while fulfilling different end user informational needs. This paper first presents a brief overview of technologies used in the water domain, and then presents three examples of Web mapping architectures based on free and open source software (FOSS) and the use of open specifications (OS) that address different users' needs for data sharing, visualization, manipulation, scenario simulations, and map production. The purpose of the paper is to illustrate how the latest developments in OS for geospatial and water-related data collection, storage, and sharing, combined with the use of mature FOSS projects facilitate the creation of sophisticated interoperable Web-based information systems in the water domain

Sensor web geoprocessing on the grid

Recent standardisation initiatives in the fields of grid computing and geospatial sensor middleware provide an exciting opportunity for the composition of large scale geospatial monitoring and prediction systems from existing components. Sensor middleware standards are paving the way for the emerging sensor web which is envisioned to make millions of geospatial sensors and their data publicly accessible by providing discovery, task and query functionality over the internet. In a similar fashion, concurrent development is taking place in the field of grid computing whereby the virtualisation of computational and data storage resources using middleware abstraction provides a framework to share computing resources. Sensor web and grid computing share a common vision of world-wide connectivity and in their current form they are both realised using web services as the underlying technological framework. The integration of sensor web and grid computing middleware using open standards is expected to facilitate interoperability and scalability in near real-time geoprocessing systems. The aim of this thesis is to develop an appropriate conceptual and practical framework in which open standards in grid computing, sensor web and geospatial web services can be combined as a technological basis for the monitoring and prediction of geospatial phenomena in the earth systems domain, to facilitate real-time decision support. The primary topic of interest is how real-time sensor data can be processed on a grid computing architecture. This is addressed by creating a simple typology of real-time geoprocessing operations with respect to grid computing architectures. A geoprocessing system exemplar of each geoprocessing operation in the typology is implemented using contemporary tools and techniques which provides a basis from which to validate the standards frameworks and highlight issues of scalability and interoperability. It was found that it is possible to combine standardised web services from each of these aforementioned domains despite issues of interoperability resulting from differences in web service style and security between specifications. A novel integration method for the continuous processing of a sensor observation stream is suggested in which a perpetual processing job is submitted as a single continuous compute job. Although this method was found to be successful two key challenges remain; a mechanism for consistently scheduling real-time jobs within an acceptable time-frame must be devised and the tradeoff between efficient grid resource utilisation and processing latency must be balanced. The lack of actual implementations of distributed geoprocessing systems built using sensor web and grid computing has hindered the development of standards, tools and frameworks in this area. This work provides a contribution to the small number of existing implementations in this field by identifying potential workflow bottlenecks in such systems and gaps in the existing specifications. Furthermore it sets out a typology of real-time geoprocessing operations that are anticipated to facilitate the development of real-time geoprocessing software.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : School of Civil Engineering & Geosciences, Newcastle UniversityGBUnited Kingdo

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

Contaminación ambiental y Geociencias. Una revisión bibliográfica

Trabajo de Fin de Máster del Máster en Geotecnologías cartográficas en ingeniería y arquitectura, curso...Con el presente trabajo, se realizará una revisión bibliográfica que permita examinar la información disponible sobre las aplicaciones y herramientas en el campo de la Geociencias las cuales aporten soluciones en la detección, prevención, seguimiento y/o modelación de eventos y agentes contaminantes del medio ambiente. Para ello se abordará una búsqueda sistémica de los últimos cinco años, que contenga información actualiza novedosa y que describa los avances más importantes durante este periodo. De la misma manera se pretende establecer la incidencia y el manejo que tienen estas herramientas en la extracción de datos fundamentales para identificar agentes contaminantes, zonas de alteración ambiental y consecuencias en los ecosistemas a fin de encontrar acciones que mitiguen y/o adapten prácticas que mejoren las condiciones del hábitat del ser humano. La estructura de este documento está conformada de la siguiente manera; En primer lugar, aparece la metodología, donde se presentan los criterios de selección y búsqueda del material bibliográfico, describiendo las bases de datos utilizadas, las palabras claves, los criterios de selección inclusión y exclusión, terminando con el diagrama de flujo que describe los pasos mencionados. En segundo lugar, se describen los resultados de la búsqueda y selección de los artículos, así como su clasificación, que para el presente trabajo se determinó así, los que aplican a grandes áreas como; Sistemas de Información Geográfica (SIG), Sensores Remotos (SR), 7 Big Data, Machine Learning (ML) y Sensores Web Geoespaciales (SGW); y los que se aplican en áreas pequeñas o casos puntuales como; Biosensores, Nariz electrónica, Ciencia ciudadana y vehículos aéreos no tripulados comúnmente conocido como drones. En seguida, se presenta el análisis y los argumentos de cada uno de los elementos bibliográficos seleccionados, así como las apreciaciones de los desarrollos tecnológicos de la Geociencias y su incidencia e importancia en el campo de los estudios de contaminación ambiental. Finalmente se presentan las conclusiones y recomendaciones del presente trabajo. Es importante mencionar que, en el proceso de análisis y selección de la información, el autor enfrenta una continua toma de decisiones las cuales constituyen en sí mismas la selección y exclusión de información que imprime un curso y dirección de argumento personal

Data Science, Data Visualization, and Digital Twins

Real-time, web-based, and interactive visualisations are proven to be outstanding methodologies and tools in numerous fields when knowledge in sophisticated data science and visualisation techniques is available. The rationale for this is because modern data science analytical approaches like machine/deep learning or artificial intelligence, as well as digital twinning, promise to give data insights, enable informed decision-making, and facilitate rich interactions among stakeholders.The benefits of data visualisation, data science, and digital twinning technologies motivate this book, which exhibits and presents numerous developed and advanced data science and visualisation approaches. Chapters cover such topics as deep learning techniques, web and dashboard-based visualisations during the COVID pandemic, 3D modelling of trees for mobile communications, digital twinning in the mining industry, data science libraries, and potential areas of future data science development