Title: Air pollution prediction through internet of things technology and big data analytics

Authors: Safae Sossi Alaoui; Brahim Aksasse; Yousef Farhaoui

Addresses: Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, M2I Laboratory, ASIA Team, B509 Boutalamine, 52000 Errachidia, Morocco ' Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, M2I Laboratory, ASIA Team, B509 Boutalamine, 52000 Errachidia, Morocco ' Department of Computer Science, Faculty of Sciences and Techniques, Moulay Ismail University, M2I Laboratory, ASIA Team, B509 Boutalamine, 52000 Errachidia, Morocco

Abstract: Air pollution is one of the biggest and serious challenges facing our planet nowadays. In fact, the need to develop models to predict this issue is considered so crucial. Indeed, our work aimed at building an accurate model to predict air quality of US country by using a dataset collected from connected devices of internet of things (IoT), namely from wireless sensor networks (WSN). Therefore, the huge amount of data captured by these sensors (approximately 1.4 million observations) brings about a highly complex data that necessitates new form of advanced analytic; it is about big data analytics. In this paper, we examine the possibility to make a fusion between the two new concepts big data and internet of things; in the context of predicting air pollution that occurs when harmful substances; like NO2, SO2, CO and O3, are introduced into Earth's atmosphere.

Keywords: internet of things; IoT; wireless sensor networks; WSNs; air pollution; air quality index; AQI; big data analytics; Apache Spark.

DOI: 10.1504/IJCISTUDIES.2019.102525

International Journal of Computational Intelligence Studies, 2019 Vol.8 No.3, pp.177 - 191

Available online: 26 Sep 2019 *

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