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Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 950)

Abstract

The currently burst of the Internet of Things (IoT) technologies implies the emergence of new lines of investigation regarding not only to hardware and protocols but also to new methods of produced data analysis satisfying the IoT environment constraints: a real-time and a big data approach. The Real-time restriction is about the continuous generation of data provided by the endpoints connected to an IoT network; due to the connection and scaling capabilities of an IoT network, the amount of data to process is so high that Big data techniques become essential. In this article, we present a system consisting of two main modules. In one hand, the infrastructure, a complete LoRa based network designed, tested and deployment in the Pablo de Olavide University and, on the other side, the analytics, a big data streaming system that processes the inputs produced by the network to obtain useful, valid and hidden information.

Keywords

  • IoT
  • LoRaWAN
  • Real-time
  • Big data
  • Data streaming

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Acknowledgments

We would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-88209-C2-1-R. Additionally, we want to express our gratitude to Enrique Parrilla, Lantia IoT’s CEO, since all the equipment has been provided by him. The T-Systems Iberia company is also acknowledged since all experiments have been carried out on its Open Telekom Cloud Platform based on the OpenStack open source.

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Correspondence to Francisco Martínez-Álvarez .

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Fernández, A.M., Gutiérrez-Avilés, D., Troncoso, A., Martínez-Álvarez, F. (2020). Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_9

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