A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring
Felix Lorenz, Morgan Geldenhuys, Harald Sommer, Frauke Jakobs, Carsten, L\"uring, Volker Skwarek, Ilja Behnke, Lauritz Thamsen

TL;DR
This paper presents a scalable, dependable data analytics platform for water infrastructure monitoring using IoT sensors, stream processing, and GIS visualization, addressing challenges of scalability and reliability in critical city infrastructure.
Contribution
It introduces a novel scalable stream processing platform integrated with energy-efficient sensors and GIS, with empirical validation and publicly available code.
Findings
Platform meets scalability and responsiveness requirements
Data enrichment improves analysis accuracy
Code and algorithms are openly accessible
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…
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