  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.
