A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring

Lorenz, F., Geldenhuys, M., Sommer, H., Jakobs, F., Lüring, C., Skwarek, V., Behnke, I. and Thamsen, L. (2020) A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 3488-3493. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378138)

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Abstract

With weather becoming more extreme both in terms of longer dry periods and more severe rain events, municipal water networks are increasingly under pressure. The effects include damages to the pipes, flash floods on the streets and combined sewer overflows. Retrofitting underground infrastructure is very expensive, thus water infrastructure operators are increasingly looking to deploy IoT solutions that promise to alleviate the problems at a fraction of the cost.In this paper, we report on preliminary results from an ongoing joint research project, specifically on the design and evaluation of its data analytics platform. The overall system consists of energy-efficient sensor nodes that send their observations to a stream processing engine, which analyzes and enriches the data and transmits the results to a GIS-based frontend. As the proposed solution is designed to monitor large and critical infrastructures of cities, several non-functional requirements such as scalability, responsiveness and dependability are factored into the system architecture. We present a scalable stream processing platform and its integration with the other components, as well as the algorithms used for data processing. We discuss significant challenges and design decisions, introduce an efficient data enrichment procedure and present empirical results to validate the compliance with the target requirements. The entire code for deploying our platform and running the data enrichment jobs is made publicly available with this paper.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Thamsen, Dr Lauritz
Authors: Lorenz, F., Geldenhuys, M., Sommer, H., Jakobs, F., Lüring, C., Skwarek, V., Behnke, I., and Thamsen, L.
College/School:College of Science and Engineering > School of Computing Science
Publisher:IEEE
ISBN:9781728162515
Published Online:19 March 2021
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 2020 IEEE International Conference on Big Data (Big Data): 3488-3493
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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