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

A comparative study in the standardisation of IoT devices using geospatial web standards

Although billions of devices are embedded in the World Wide Web through the Internet of Things, there is still a lack of a common, interoperable way to connect them and make them interact seamlessly. IoT has also found its way into the spatial web. Environmental monitoring and sensing platforms connected over the web by wireless sensor networks are now a common way to monitor natural phenomena. This study compares two open Web Standards ( OGC’s Sensor Observation Service and SensorThings API ) from the geospatial point of view. An IoT platform, called SEnviro , is used to integrate and evaluate implementations for each standard and contrast their qualitative and quantitative traits. The results of the study show that the SensorThings API proves to be the adequate Web Standard for IoT applications in terms of interoperability. It outperforms the contesting Web Standard in terms of flexibility and scalability, which strongly impacts on developer and user experience

Interoperability enhancement of IoT devices using open web standards in a smart farming use case

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSince its rst appearance the Internet of Things has been subject to constant evolution, development and change. Now it has stepped out of its infancy with billions of devices embedded in the world wide web. However, IoT providers mostly de ne their own data formats and protocols and there is still a lack of a common standard that connects these devices in an interoperable manner. There are several organisations dedicated to developing common standards for IoT devices and research is focusing on de ning an e ective standard to be used by embedded devices. Unsurprisingly, IoT has also found its way into the spatial web and into environmental monitoring and sensing platforms connected over the web by wireless sensor networks are now a common way to monitor natural phenomena. This study compares three open Web Standards in the use case of SEnviro for Agriculture, a full stack IoT for monitoring vineyards. The interoperability potential of the OGC's Sensor Observation Service and SensorThings API are evaluated by integrating Web Standard implementations for each standard and contrasting their qualitative and quantitative traits. In a further step the Mozilla Corporation's Web Thing API was implemented and evaluated in an environmental monitoring and Smart Farming context. The results of the study show that the SensorThings API proves to be the most adequate Web Standard for SEnviro and IoT applications for environmental monitoring and Smart Farming in terms of interoperability. It outperforms the contesting Web Standards in terms of exibility and scalability, which strongly impacts on developer and user experience

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Consuming data sources to generate actionable items

Plataforma que consumeixi sensors IoT i sistemes d'alertes per a generar accions de resposta relacionades amb els sistemes d'alerta. Per a demostrar els casos d'ús possibles s'incorporaran funcions requerides per Projectes Europeus, solucions comercials i solucions compatibles amb estàndards

AUTOMATIC ASSESSMENT OF LAKE STATUS USING AN OPEN SOURCE APPROACH: LAKE LUGANO'S CASE STUDY

Abstract. Climate change and human activities are increasingly threatening water resources. In particular sub-alpine lakes are fundamental not only for tourism or other economical activities, but also as a source of water. In this context, there is a strong need to monitor such resources to understand, study and react to known and unknown impacts, so that appropriate mitigation actions can be taken. Unfortunately, although monitoring data already exist for many of these lakes, the information is archived in different formats and servers undermining the full exploitation of data and preventing a more efficient data management. The aim of this work is to improve this situation by implementing a system that integrates and standardizes data coming from different sources. In addition, the system integrates web based tools that estimate lake state indicators using open source software and standard. Thanks to this system, it will be possible to exploit the data potential more fully. This paper focuses on the achievements reached by the research carried out on Lake Lugano in the context of the project SIMILE after two years of work

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