A Comparison of Time Series Databases for Storing Water Quality Data

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Section 1: Publication

Publication Type

Authorship

Fadhel, M., Sekerinski, E., & Yao, S.

Title

A Comparison of Time Series Databases for Storing Water Quality Data

Year

2019

Publication Outlet

In M. E. Auer & T. Tsiatsos (Eds.), Mobile Technologies and Applications for the Internet of Things (pp. 302–313). Springer.

DOI

https://doi.org/10.1007/978-3-030-11434-3_33

ISBN

Print ISBN: 978-3-030-11433-6, online ISBN: 978-3-030-11434-3

ISSN

Citation

Fadhel, M., Sekerinski, E., & Yao, S. (2019). A Comparison of Time Series Databases for Storing Water Quality Data. In M. E. Auer & T. Tsiatsos (Eds.), Mobile Technologies and Applications for the Internet of Things (pp. 302–313). Springer. https://doi.org/10.1007/978-3-030-11434-3_33

Abstract

Water quality is an ongoing concern and wireless water quality sensing promises societal benefits. Our goal is to contribute to a low-cost water quality sensing system. The particular focus of this work is the selection of a database for storing water quality data. Recently, time series databases have gained popularity. This paper formulates criteria for comparison, measures selected databases and makes a recommendation for a specific database. A low-cost low-power server, such as a Raspberry Pi, can handle as many as 450 sensors’ data at the same time by using the InfluxDB time series database.

Plain Language Summary

T-2023-09-22-81roAMIXArkCJxDcdOFmMNg Publication 1.0