Extending INSPIRE to the Internet of Things through SensorThings API

Kotsev, Alexander; Schleidt, Katharina; Liang, Steve H. L.; Schaaf, Hylke van der; Khalafbeigi, Tania; Grellet, Sylvain; Lutz, Michael; Jirka, Simon; Beaufils, Mickaël

doi:10.3390/geosciences8060221

Cited by 28 publications

(9 citation statements)

References 6 publications

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“…OGC recently developed SensorThings standard to help overcome the interoperability challenge in the IoT domain [111]. SensorThings uses OGC's Observations and Measurement standard as data model, and defines a REST-like application programming interface (API) to interconnect IoT devices over the Web, and to interact with and analyze their observations [112]. The characteristics of Big Data, however, often require data standardization and other data wrangling tasks to be performed more efficiently.…”

Section: Problems Big Data Can Tackle But With Some Helpmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

340

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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Section: Problems Big Data Can Tackle But With Some Helpmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

340

129

View full text Add to dashboard Cite

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“…Good examples of such a lightweight approach are the 'OGC API -Features' and 'SensorThings API' standards [69,70]. Both standards have huge potential for modernising SDIs and are already considered as possible INSPIRE Download Services [74,75].…”

Section: Grassroots Standardisationmentioning

confidence: 99%

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

Kotsev

Minghini

Tomáš

et al. 2020

IJGI

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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.

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“…Finally, SensorThingsAPI makes cities complaint with the INSPIRE Directive by extending INSPIRE to the IoT [44]. The European Union INSPIRE Directive laid down Spatial Data SonsorThingAPI is able to overcome these specifications, and this, benefits the implementation of Urban Data Platforms to be interoperable, standard and INSPIRE-compliant.…”

Section: Semantic Interoperability Via Sensorthingsapi Semantic Modelmentioning

confidence: 99%

“…In this way, third party data consumers, such as services or developers, require data interpretation mechanisms. That is the reason why, within the interoperability layer, for providing open APIs and SDKs, JSON-LD (JSON Linked Data) [44] is also considered to add the capability of self-discovering data.…”

Section: Data Sharing: Metadata For Structural Interoperabilitymentioning

confidence: 99%

Interoperable Open Specifications Framework for the Implementation of Standardized Urban Platforms

Hernández¹,

García²,

Schonowski³

et al. 2020

Sensors

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The current cities' urban challenges go through digitalization and integration of new technologies under the perspective of actual and future ecological, as well as socio-economic commitments. This process is translated into the Open Standardized Urban Data Platform, which plays a pivotal role. Within its main functionalities, data ingestion, analytics and services as vertical domains become necessary to create more environmentally friendly cities. However, there still exist some deficits. Among them, openness and interoperability are outlined. On the one hand, there is a lack of open data initiatives for increasing the smart services stock. On the other hand, interoperability depends upon vendors and integrators, reducing the possibilities of Smart City growth. In this context, under the mySMARTLife project (GA #731297) umbrella, an Open Specifications Framework has been developed in order to address four main issues: (1) data interoperability; (2) services or verticals interoperability; (3) openness; and (4) replicability. It enlightens the implementation and integration of multiple city domains (like infrastructures, mobility, energy, buildings) for smart management and big-data analytics. Its applicability is demonstrated in the three lighthouse cities of the project, Nantes (France), Hamburg (Germany) and Helsinki (Finland).

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Extending INSPIRE to the Internet of Things through SensorThings API

Cited by 28 publications

References 6 publications

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

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

Interoperable Open Specifications Framework for the Implementation of Standardized Urban Platforms

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