Real-time monitoring of the Great Barrier Reef using Internet of Things with big data analytics /

Autor(es):
Palaniswami, Marimuthu | Rao, Aravinda S | Bainbridge, Scott
Editor: Geneva : International Telecommunication Union, 2017Descripción: 10 pTipo de contenido: texto (visual)
Tipo de medio: electrónico
Tipo de soporte: recurso en línea
Tema(s): Tecnologías habilitadoras digitales | Science and TechnologyRecursos en línea: Acceso al documento En: ITU Journal: ICT Discoveries Vol. 2018, no. 1, p. 23-32Resumen: The Great Barrier Reef (GBR) of Australia is the largest size of coral reef system on the planet stretching over 2300 kilometers. Coral reefs are experiencing a range of stresses including climate change, which has resulted in episodes of coral bleaching and ocean acidification where increased levels of carbon dioxide from the burning of fossil fuels are reducing the calcification mechanism of corals. In this article, we present a successful application of big data analytics with Internet of Things (IoT)/wireless sensor networks (WSNs) technology to monitor complex marine environments of the GBR. The paper presents a two-tiered IoT/WSN network architecture used to monitor the GBR and the role of artificial intelligence (AI) algorithms with big data analytics to detect events of interest. The case study presents the deployment of a WSN at Heron Island in the southern GBR in 2009. It is shown that we are able to detect Cyclone Hamish patterns as an anomaly using the sensor time series of temperature, pressure and humidity data. The article also gives a perspective of AI algorithms from the viewpoint to monitor, manage and understand complex marine ecosystems. The knowledge obtained from the large-scale implementation of IoT with big data analytics will continue to act as a feedback mechanism for managing a complex system of systems (SoS) in our marine ecosystem.
    Valoración media: 0.0 (0 votos)
Tipo de ítem Ubicación actual Colección Signatura Estado Notas Fecha de vencimiento Código de barras
Artículos Artículos CDO

El Centro de Documentación del Observatorio Nacional de las Telecomunicaciones y de la Sociedad de la Información (CDO) os da la bienvenida al catálogo bibliográfico sobre recursos digitales en las materias de Tecnologías de la Información y telecomunicaciones, Servicios públicos digitales, Administración Electrónica y Economía digital. 

 

 

Colección digital Acceso libre online pdf 1000020175579

The Great Barrier Reef (GBR) of Australia is the largest size of coral reef system on the planet stretching over 2300 kilometers. Coral reefs are experiencing a range of stresses including climate change, which has resulted in episodes of coral bleaching and ocean acidification where increased levels of carbon dioxide from the burning of fossil fuels are reducing the calcification mechanism of corals. In this article, we present a successful application of big data analytics with Internet of Things (IoT)/wireless sensor networks (WSNs) technology to monitor complex marine environments of the GBR. The paper presents a two-tiered IoT/WSN network architecture used to monitor the GBR and the role of artificial intelligence (AI) algorithms with big data analytics to detect events of interest. The case study presents the deployment of a WSN at Heron Island in the southern GBR in 2009. It is shown that we are able to detect Cyclone Hamish patterns as an anomaly using the sensor time series of temperature, pressure and humidity data. The article also gives a perspective of AI algorithms from the viewpoint to monitor, manage and understand complex marine ecosystems. The knowledge obtained from the large-scale implementation of IoT with big data analytics will continue to act as a feedback mechanism for managing a complex system of systems (SoS) in our marine ecosystem.

No hay comentarios en este titulo.

para colocar un comentario.

Haga clic en una imagen para verla en el visor de imágenes

Copyright© ONTSI. Todos los derechos reservados.
x
Esta web está utilizando la política de Cookies de la entidad pública empresarial Red.es, M.P. se detalla en el siguiente enlace: aviso-cookies. Acepto