SAT-CEP-monitor: An air quality monitoring software architecture combining complex event processing with satellite remote sensing

Semlali, Badr-Eddine Boudriki; Amrani, Chaker El; Ortiz, Guadalupe; Boubeta-Puig, Juan; Garcia-de-Prado, Alfonso

doi:10.1016/j.compeleceng.2021.107257

Cited by 25 publications

(7 citation statements)

References 24 publications

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“…Other ML classification tools used in water quality applications include the K-nearest neighbor (KNN), single layer, and deep neural networks. Software architectures that combine event processing with remote sensing applications for air quality monitoring using satellite sensors were proposed by [22].…”

Section: Related Workmentioning

confidence: 99%

WSN Architectures for Environmental Monitoring Applications

et al. 2022

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Wireless sensor networks (WSNs) have become ubiquitous, permeating every aspect of human life. In environmental monitoring applications (EMAs), WSNs are essential and provide a holistic view of the deployed environment. Physical sensor devices and actuators are connected across a network in environmental monitoring applications to sense vital environmental factors. EMAs bring together the intelligence and autonomy of autonomous systems to make intelligent decisions and communicate them using communication technologies. This paper discusses the various architectures developed for WSNs in environmental monitoring applications and the support for specific design goals, including machine learning in WSNs and its potential in environmental monitoring applications.

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Section: Related Workmentioning

confidence: 99%

WSN Architectures for Environmental Monitoring Applications

et al. 2022

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“…RS techniques have been broadly used in many environmental applications, such as AP monitoring [1] and climate change monitoring. Satellites are the primary equipment for the measurement.…”

Section: Rsbd Specificationmentioning

confidence: 99%

“…Remote Sensing (RS) refers to the technique of observing atmospheric objects remotely. Conventionally, RS was used for satellite and airborne platforms, obtaining data from optical and radar sensors [1]. Formerly, more than 3000 satellites in orbit were used in several applications.…”

Section: Introductionmentioning

confidence: 99%

SAT-Hadoop-Processor: A Distributed Remote Sensing Big Data Processing Software for Earth Observation Applications

Semlali

Freitag²

2021

Applied Sciences

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Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.

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“…In addition to ground monitoring stations, satellites are increasingly being used for monitoring air quality and tracking air pollution sources. The increasing availability of remotely sensed data obtained using advanced sensors with high resolutions made satellite-based data highly useful for monitoring air pollutants and physical conditions of the Earth's surface ( Boudriki Semlali and Amrani, 2021 ; Semlali and Amrani, 2020 ; Semlali et al, 2021 ; Semlali and El Amrani, 2021 ; Weigand et al, 2019 ). Moreover, metrological and satellite-based derivatives are incorporated in various studies to elucidate the spatiotemporal patterns of air pollutants, biophysical parameters, and the associated respiratory diseases ( Alvarez-Mendoza et al, 2020 ; Chang et al, 2019 ; Davila Cordova et al, 2020 ; de Souza, 2019 ; Sun et al, 2020 ; Yitshak-Sade et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%