2016
Granell-Canut, Carlos; Havlik, Denis; Schade, Sven; Sabeur, Zoheir; Delaney, Conor; Pielorz, Jasmin; Usländer, Thomas; Mazzetti, Paolo; Schleidt, Katharina; Kobernus, Mike; Havlik, Fuada; Bodsberg, Nils Rune; Berre, Arne; Mon, Jose Lorenzo
Future Internet technologies for environmental applications Journal Article
In: Environmental Modelling & Software, vol. 78, pp. 1 - 15, 2016, ISSN: 1364-8152, (IF: 4.404 - 6/105 (Q1) Computer Science, Interdisciplinary Applications IF: 4.404 - 8/49 (Q1) Engineering, Environmental IF: 4.404 - 25/229 (Q1) Environmental Sciences ).
Abstract | Links | BibTeX | Tags: Big data, Cloud computing, Crowdtasking, Environmental informatics, Environmental observation web, Environmental specific enablers, Future internet, Internet of things, Volunteered geographic information
@article{Granell20161,
title = {Future Internet technologies for environmental applications},
author = {Carlos Granell-Canut and Denis Havlik and Sven Schade and Zoheir Sabeur and Conor Delaney and Jasmin Pielorz and Thomas Usländer and Paolo Mazzetti and Katharina Schleidt and Mike Kobernus and Fuada Havlik and Nils Rune Bodsberg and Arne Berre and Jose Lorenzo Mon},
url = {http://www.sciencedirect.com/science/article/pii/S1364815215301298},
doi = {http://dx.doi.org/10.1016/j.envsoft.2015.12.015},
issn = {1364-8152},
year = {2016},
date = {2016-04-08},
journal = {Environmental Modelling & Software},
volume = {78},
pages = {1 - 15},
abstract = {This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.},
note = {IF: 4.404 - 6/105 (Q1) Computer Science, Interdisciplinary Applications
IF: 4.404 - 8/49 (Q1) Engineering, Environmental
IF: 4.404 - 25/229 (Q1) Environmental Sciences
},
keywords = {Big data, Cloud computing, Crowdtasking, Environmental informatics, Environmental observation web, Environmental specific enablers, Future internet, Internet of things, Volunteered geographic information},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.
2015
Trilles-Oliver, Sergio; Schade, Sven; Belmonte-Fernández, Óscar; Huerta-Guijarro, Joaquín
Real-Time Anomaly Detection from Environmental Data Streams Book Section
In: Baçao, Fernando; Santos, Maribel Yasmina; Painho, Marci (Ed.): AGILE 2015: Geographic information science as an enabler of smarter cities and communities, pp. 125–144, Springer International Publishing, Heidelberg, 2015, ISBN: 978-3-319-16786-2.
Abstract | Links | BibTeX | Tags: Big data, Real time analysis, Sensors
@incollection{TrillesOliver2015b,
title = {Real-Time Anomaly Detection from Environmental Data Streams},
author = {Sergio Trilles-Oliver and Sven Schade and Óscar Belmonte-Fernández and Joaquín Huerta-Guijarro},
editor = {Baçao, Fernando and Santos, Maribel Yasmina and Painho, Marci},
url = {http://link.springer.com/10.1007/978-3-319-16787-9_8},
doi = {10.1007/978-3-319-16787-9_8},
isbn = {978-3-319-16786-2},
year = {2015},
date = {2015-01-01},
booktitle = {AGILE 2015: Geographic information science as an enabler of smarter cities and communities},
pages = {125--144},
publisher = {Springer International Publishing},
address = {Heidelberg},
abstract = {Modern sensor networks monitor a wide range of phenomena. They are applied in environmental monitoring, health care, optimization of industrial processes, social media, smart city solutions, and many other domains. All in all, they provide a continuously pulse of the almost infinite activities that are happening in the physical space—and in cyber space. The handling of the massive amounts of generated measurements poses a series of (Big Data) challenges. Our work addresses one of these challenges: the detection of anomalies in real-time. In this paper, we propose a generic solution to this problem, and introduce a system that is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user. We apply CUSUM a statistical control algorithm and adopt it so that it can be used inside the Storm framework—a robust and scalable real-time processing framework. We present a proof of concept implementation from the area of environmental monitoring.},
keywords = {Big data, Real time analysis, Sensors},
pubstate = {published},
tppubtype = {incollection}
}
Modern sensor networks monitor a wide range of phenomena. They are applied in environmental monitoring, health care, optimization of industrial processes, social media, smart city solutions, and many other domains. All in all, they provide a continuously pulse of the almost infinite activities that are happening in the physical space—and in cyber space. The handling of the massive amounts of generated measurements poses a series of (Big Data) challenges. Our work addresses one of these challenges: the detection of anomalies in real-time. In this paper, we propose a generic solution to this problem, and introduce a system that is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user. We apply CUSUM a statistical control algorithm and adopt it so that it can be used inside the Storm framework—a robust and scalable real-time processing framework. We present a proof of concept implementation from the area of environmental monitoring.