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

Development of a spatial data infrastructure for precision agriculture applications

Precision agriculture (PA) is the technical answer to tackling heterogeneous conditions in a field. It works through site specific operations on a small scale and is driven by data. The objective is an optimized agricultural field application that is adaptable to local needs. The needs differ within a task by spatial conditions. A field, as a homogenous-planted unit, exceeds by its size the scale units of different landscape ecological properties, like soil type, slope, moisture content, solar radiation etc. Various PA-sensors sample data of the heterogeneous conditions in a field. PA-software and Farm Management Information Systems (FMIS) transfer the data into status information or application instructions, which are optimized for the local conditions. The starting point of the research was the determination that the process of PA was only being used in individual environments without exchange between different users and to other domains. Data have been sampled regarding specific operations, but the model of PA suffers from these closed data streams and software products. Initial sensors, data processing and controlled implementations were constructed and sold as monolithic application. An exchange of hard- or software as well as of data was not planned. The design was focused on functionality in a fixed surrounding and conceived as being a unit. This has been identified as a disadvantage for ongoing developments and the creation of added value. Influences from the outside that may be innovative or even inspired cannot be considered. To make this possible, the underlying infrastructure must be flexible and optimized for the exchange of data. This thesis explores the necessary data handling, in terms of integrating knowledge of other domains with a focus on the geo-spatial data processing. As PA is largely dependent on geographical data, this work develops spatial data infrastructure (SDI) components and is based on the methods and tools of geo-informatics. An SDI provides concepts for the organization of geospatial components. It consists of spatial- and metadata in geospatial workflows. The SDI in the center of these workflows is implemented by technologies, policies, arrangements, and interfaces to make the data accessible for various users. Data exchange is the major aim of the concept. As previously stated, data exchange is necessary for PA operations, and it can benefit from defined components of an SDI. Furthermore, PA-processes gain access to interchange with other domains. The import of additional, external data is a benefit. Simultaneously, an export interface for agricultural data offers new possibilities. Coordinated communication ensures understanding for each participant. From the technological point of view, standardized interfaces are best practice. This work demonstrates the benefit of a standardized data exchange for PA, by using the standards of the Open Geospatial Consortium (OGC). The OGC develops and publishes a wide range of relevant standards, which are widely adopted in geospatially enabled software. They are practically proven in other domains and were implemented partially in FMIS in the recent years. Depending on their focus, they could support software solutions by incorporating additional information for humans or machines into additional logics and algorithms. This work demonstrates the benefits of standardized data exchange for PA, especially by the standards of the OGC. The process of research follows five objectives: (i) to increase the usability of PA-tools in order to open the technology for a wider group of users, (ii) to include external data and services seamlessly through standardized interfaces to PA-applications, (iii) to support exchange with other domains concerning data and technology, (iv) to create a modern PA-software architecture, which allows new players and known brands to support processes in PA and to develop new business segments, (v) to use IT-technologies as a driver for agriculture and to contribute to the digitalization of agriculture.Precision agriculture (PA) ist die technische Antwort, um heterogenen Bedingungen in einem Feld zu begegnen. Es arbeitet mit teilflächenspezifischen Handlungen kleinräumig und ist durch Daten angetrieben. Das Ziel ist die optimierte landwirtschaftliche Feldanwendung, welche an die lokalen Gegebenheiten angepasst wird. Die Bedürfnisse unterscheiden sich innerhalb einer Anwendung in den räumlichen Bedingungen. Ein Feld, als gleichmäßig bepflanzte Einheit, überschreitet in seiner Größe die räumlichen Einheiten verschiedener landschaftsökologischer Größen, wie den Bodentyp, die Hangneigung, den Feuchtigkeitsgehalt, die Sonneneinstrahlung etc. Unterschiedliche Sensoren sammeln Daten zu den heterogenen Bedingungen im Feld. PA-Software und farm management information systems (FMIS) überführen die Daten in Statusinformationen oder Bearbeitungsanweisungen, die für die Bedingungen am Ort optimiert sind. Ausgangspunkt dieser Dissertation war die Feststellung, dass der Prozess innerhalb von PA sich nur in einer individuellen Umgebung abspielte, ohne dass es einen Austausch zwischen verschiedenen Nutzern oder anderen Domänen gab. Daten wurden gezielt für Anwendungen gesammelt, aber das Modell von PA leidet unter diesen geschlossenen Datenströmen und Softwareprodukten. Ursprünglich wurden Sensoren, die Datenverarbeitung und die Steuerung von Anbaugeräten konstruiert und als monolithische Anwendung verkauft. Ein Austausch von Hard- und Software war ebenso nicht vorgesehen wie der von Daten. Das Design war auf Funktionen in einer festen Umgebung ausgerichtet und als eine Einheit konzipiert. Dieses zeigte sich als Nachteil für weitere Entwicklungen und bei der Erzeugung von Mehrwerten. Äußere innovative oder inspirierende Einflüsse können nicht berücksichtigt werden. Um dieses zu ermöglichen muss die darunterliegende Infrastruktur flexibel und auf einen Austausch von Daten optimiert sein. Diese Dissertation erkundet die notwendige Datenverarbeitung im Sinne der Integration von Wissen aus anderen Bereichen mit dem Fokus auf der Verarbeitung von Geodaten. Da PA sehr abhängig von geographischen Daten ist, werden in dieser Arbeit die Bausteine einer Geodateninfrastruktur (GDI) entwickelt, die auf den Methoden undWerkzeugen der Geoinformatik beruhen. Eine GDI stellt Konzepte zur Organisation räumlicher Komponenten. Sie besteht aus Geodaten und Metadaten in raumbezogenen Arbeitsprozessen. Die GDI, als Zentrum dieser Arbeitsprozesse, wird mit Technologien, Richtlinien, Regelungen sowie Schnittstellen, die den Zugriff durch unterschiedliche Nutzer ermöglichen, umgesetzt. Datenaustausch ist das Hauptziel des Konzeptes. Wie bereits erwähnt, ist der Datenaustausch wichtig für PA-Tätigkeiten und er kann von den definierten Komponenten einer GDI profitieren. Ferner bereichert der Austausch mit anderen Gebieten die PA-Prozesse. Der Import zusätzlicher Daten ist daher ein Gewinn. Gleichzeitig bietet eine Export-Schnittstelle für landwirtschaftliche Daten neue Möglichkeiten. Koordinierte Kommunikation sichert das Verständnis für jeden Teilnehmer. Aus technischer Sicht sind standardisierte Schnittstellen die beste Lösung. Diese Arbeit zeigt den Gewinn durch einen standardisierten Datenaustausch für PA, indem die Standards des Open Geospatial Consortium (OGC) genutzt wurden. Der OGC entwickelt und publiziert eine Vielzahl von relevanten Standards, die eine große Reichweite in Geo-Software haben. Sie haben sich in der Praxis anderer Bereiche bewährt und wurden in den letzten Jahren teilweise in FMIS eingesetzt. Abhängig von ihrer Ausrichtung könnten sie Softwarelösungen unterstützen, indem sie zusätzliche Informationen für Menschen oder Maschinen in zusätzlicher Logik oder Algorithmen integrieren. Diese Arbeit zeigt die Vorzüge eines standardisierten Datenaustauschs für PA, insbesondere durch die Standards des OGC. Die Ziele der Forschung waren: (i) die Nutzbarkeit von PA-Werkzeugen zu erhöhen und damit die Technologie einer breiteren Gruppe von Anwendern verfügbar zu machen, (ii) externe Daten und Dienste ohne Unterbrechung sowie über standardisierte Schnittstellen für PA-Anwendungen einzubeziehen, (iii) den Austausch mit anderen Bereichen im Bezug auf Daten und Technologien zu unterstützen, (iv) eine moderne PA-Softwarearchitektur zu erschaffen, die es neuen Teilnehmern und bekannten Marken ermöglicht, Prozesse in PA zu unterstützen und neue Geschäftsfelder zu entwickeln, (v) IT-Technologien als Antrieb für die Landwirtschaft zu nutzen und einen Beitrag zur Digitalisierung der Landwirtschaft zu leisten

Optimizing precision agricultural operations by standardized cloud-based functions

Aim of study: An approach to integrate knowledge into the IT-infrastructure of precision agriculture (PA) is presented. The creation of operation relevant information is analyzed and explored to be processed by standardized web services and thereby to integrate external knowledge into PA. The target is to make knowledge integrable into any software solution. Area of study: The data sampling took place at the Heidfeld Hof Research Station in Stuttgart, Germany. Material and methods: This study follows the information science’s idea to separate the process from data sampling into the final actuation through four steps: data, information, knowledge, and wisdom. The process from the data acquisition, over a professional data treatment to the actual application is analyzed by methods modelled in the Unified Modelling Language (UML) for two use-cases. It was further applied for a low altitude sensor in a PA operation; a data sampling by UAV represents the starting point. Main results: For the implemented solution, the Web Processing Service (WPS) of the Open Geospatial Consortium (OGC) is proposed. This approach reflects the idea of a function as a service (FaaS), in order to develop a demand-driven and extensible solution for irregularly used functionalities. PA benefits, as on-farm processes are season oriented and a FaaS reflects the farm’s variable demands over time by origin and extends the concept to offer external know-how for the integration into specific processes. Research highlights: The standardized implementation of knowledge into PA software products helps to generate additional benefits for PA

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

Developing an open data portal for the ESA climate change initiative

We introduce the rationale for, and architecture of, the European Space Agency Climate Change Initiative (CCI) Open Data Portal (http://cci.esa.int/data/). The Open Data Portal hosts a set of richly diverse datasets – 13 “Essential Climate Variables” – from the CCI programme in a consistent and harmonised form and to provides a single point of access for the (>100 TB) data for broad dissemination to an international user community. These data have been produced by a range of different institutions and vary across both scientific and spatio-temporal characteristics. This heterogeneity of the data together with the range of services to be supported presented significant technical challenges. An iterative development methodology was key to tackling these challenges: the system developed exploits a workflow which takes data that conforms to the CCI data specification, ingests it into a managed archive and uses both manual and automatically generated metadata to support data discovery, browse, and delivery services. It utilises both Earth System Grid Federation (ESGF) data nodes and the Open Geospatial Consortium Catalogue Service for the Web (OGC-CSW) interface, serving data into both the ESGF and the Global Earth Observation System of Systems (GEOSS). A key part of the system is a new vocabulary server, populated with CCI specific terms and relationships which integrates OGC-CSW and ESGF search services together, developed as part of a dialogue between domain scientists and linked data specialists. These services have enabled the development of a unified user interface for graphical search and visualisation – the CCI Open Data Portal Web Presence

Sensorml-Nt: Innovative Cloud Service Sensor Description For Mobile Devices Handling Environmental Issues.

Peranti mudah alih hari ini boleh didapati di mana-mana dan semakin banyak dilengkapi dengan set pengesan terbenam yang berkuasa. Today’s mobile devices are pervasive and equipped with growing sets of powerful embedded sensors

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Modifications To Web Processing Service Standard For Client-Side Geoprocessing

Nowadays we see the rapid growth of solutions number for geospatial data processing in the Web (i.e. geoprocessing). One of the main trends of Web geotechnologies evolution is the transition from Web map applications to the Web GIS applications, which are supplement the maps delivery with the analytic tools providing to the end user through Web interface. In fact, the only general open standard describes implementation rules for Web geoprocessing services. This is the Open Geospatial Consortium Web Processing Service standard, which is fully server-oriented. Moreover, the vast majority of currently used solutions (both open source and proprietary) are server-oriented, i.e. assume the server resources only as the computational resource. However, some researchers underline that it is possible way to transmit the executable code to the client for client-side computations and geoprocessing. Also, some general Web architecture concepts assume the effectiveness of client-side computations, e.g. Fog Computing concept. Our practical experience also shows that in some cases it is useful to have ability of client-side geoprocessing, which is not opposite but complement technology to the server-side processing technologies. In addition, we believe that it is more useful to have the ability to run the same processing tool by choice on server or client side. We name such double-sided services as Hybrid Geoprocessing Web Services. We study and discuss the approaches to gap filling in client-side geoprocessing general schema. For this purpose, we implemented previously the getProcess request as addition to the WPS protocol. Additionally at the previous steps of our study, we proposed a possible structure of getProcess request and draft XML file structure for its response, which describes the list of executable resources and their dependencies. Currently we working on detailed methodology of processing tools implementation and testing. We use the Python programming language as primary development tool, because of its applicability to build both server- and client-side crossplatform processing tools using single core program code. We use Python also for implementation of needed infrastructure components, such as HGWS server that supports the getProcess request/response performing, and client-side Runtime Environment that provides executable code orchestration on the client. Achieved results need to be discussed widely and carefully. However, main conclusion of our current work is that client-side geoprocessing schema in general could be relatively simple and compatible backward with current standards. The HGWS concept is applicable when implementing client-side geoprocessing Web services in small-scale projects and could be the entering point for study of distributed geoprocessing systems implementation

Sensor grid middleware metamodeling and analysis

Sensor grid is a platform that combines wireless sensor networks and grid computing with the aim of exploiting the complementary advantages of the two systems. Proper integration of these distinct systems into effective, logically single platform is challenging. This paper presents an approach for practical sensor grid implementation and management. The proposed approach uses a metamodeling technique and performance analysis and tuning as well as a middleware infrastructure that enable practical sensor grid implementation and management. The paper presents our implementation and analysis of the sensor grid. © 2014 Srimathi Chandrasekaran et al