A domain-independent methodology to analyze IoT data streams in real-time. A proof of concept implementation for anomaly detection from environmental data

Pushed by the Internet of Things (IoT) paradigm modern sensor networks monitor a wide range of phenomena, in areas such as environmental monitoring, health care, industrial processes, and smart cities. These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus, key enablers for a Digital Earth Nervous System. Nevertheless, the rapid processing of these sensor data streams still continues to challenge traditional data handling solutions and new approaches are being requested. We propose a generic answer to this challenge, which has the potential to support any form of distributed real-time analysis. This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability. As a proof of concept, we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring. The developed system is capable of detecting anomalies, generating notifications, and displaying the recent situation to the user.

TRILLES Sergi;  BELMONTE Oscar;  SCHADE Sven;  HUERTA Joaquin; 

2016-12-01

TAYLOR & FRANCIS LTD

JRC102378

1753-8947,   

http://www.tandfonline.com/doi/full/10.1080/17538947.2016.1209583,    https://publications.jrc.ec.europa.eu/repository/handle/JRC102378,   

10.1080/17538947.2016.1209583,