A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network

A Big Data Test-bed for Analyzing Data Generated by an Air Pollution Sensor Network

Lídice García Ríos (Instituto Tecnológico Autónomo de México, Mexico City, Mexico) and José Alberto Incera Diéguez (Instituto Tecnológico Autónomo de México, Mexico City, Mexico)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJWSR.2016100102
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Sensor networks have perceived an extraordinary growth in the last few years. From niche industrial and military applications, they are currently deployed in a wide range of settings as sensors are becoming smaller, cheaper and easier to use. Sensor networks are a key player in the so-called Internet of Things, generating exponentially increasing amounts of data. Nonetheless, there are very few documented works that tackle the challenges related with the collection, manipulation and exploitation of the data generated by these networks. This paper presents a proposal for integrating Big Data tools (in rest and in motion) for gathering, storage and analysis of data generated by a sensor network that monitors air pollution levels in a city. The authors provide a proof of concept that combines Hadoop and Storm for data processing, storage and analysis, and Arduino-based kits for constructing their sensor prototypes.
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WSN can be used to solve many kinds of problems and help optimizing existing processes and tasks. Well-known cases include environmental, like precision agriculture, natural disaster prevention and pollution monitoring and analysis; medical, military and industrial applications, like urban location, traffic control, home automation, structural analysis, etc. (Desai, Jain et al. 2011; Antachoque Espinoza, 2011; Clifford K., Robinson, R. et al. 2005; Shnayder, Borrong et al. 2005).

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