uncertainties did not seem to greatly affect the placement of the sensors. Worth of
note in this study is also the fact that the authors proposed to simulate leaks/bursts
that start at different times during the day by discretising the demand patterns into
four clusters. Bearing in mind that the authors stressed that this discretisation was
only necessary to mitigate the computational burden that would be faced if leaks/
bursts were allowed to start at every time step of a hydraulic simulation, their attempt
to account for more realistic leak/burst modelling assumptions is valuable and
highlights a further source of uncertainty that has been somehow neglected by
optimal sensor placement studies.
3.3 Sensor and Communication Failures
Sensor networks are exposed to failure conditions, such as sensor malfunctions and
communication system failures. In the current hyperconnected world, for example,
cyberattacks are now a major risk for sensor and communication malfunctions/
failures [88]. Therefore, a sensor network’s robustness should be considered for
the reliable provision of informative sensor data. A sensor network’s robustness
should be considered at the design stage because the overall information gain and,
hence, the effectiveness of the sensor network should be assessed as a whole.
Despite the information gain varies for different locations, information gains from
data collected at some locations can compensate for those at other locations [89].
The vast majority of optimal sensor placement methods that can be found in the
literature have assumed that all sensors perform without any failure. However, this
assumption is not realistic and may result in the design of a sensor network that
performs poorly when the network is partially impaired (e.g. a sensor fails).
In the above context, Jung and Kim [89] proposed a leak/burst detection approach
similar to that proposed by Hagos et al. [52] but that builds on that work by (1) using
the NSGA-II for multi-objective optimal sensor placement and, most significantly,
by (2) introducing a further criterion in that optimisation, namely, the maximisation
of the robustness of a sensor network given a predefined number of sensors. The
authors defined the sensor network’s robustness as its ability to consistently provide
quality data in the event of sensor failure. Individual sensor failures were considered
in that study, and a coefficient of variation of the rate of correct detections in the
event of a sensor failure was used to assess the variation in performances of the
subsets of a given set of sensors. The authors tested this method on the same
synthetic network used in Hagos et al. [52], performed experiments with very similar
settings and considered a similar number of pressure and flow sensors to be independently deployed. By accounting for robustness of a sensor network, quite
different sensor placements were proposed, thereby confirming that a sensor network’s robustness should be considered at the sensor network’s design stage.
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
43
note in this study is also the fact that the authors proposed to simulate leaks/bursts
that start at different times during the day by discretising the demand patterns into
four clusters. Bearing in mind that the authors stressed that this discretisation was
only necessary to mitigate the computational burden that would be faced if leaks/
bursts were allowed to start at every time step of a hydraulic simulation, their attempt
to account for more realistic leak/burst modelling assumptions is valuable and
highlights a further source of uncertainty that has been somehow neglected by
optimal sensor placement studies.
3.3 Sensor and Communication Failures
Sensor networks are exposed to failure conditions, such as sensor malfunctions and
communication system failures. In the current hyperconnected world, for example,
cyberattacks are now a major risk for sensor and communication malfunctions/
failures [88]. Therefore, a sensor network’s robustness should be considered for
the reliable provision of informative sensor data. A sensor network’s robustness
should be considered at the design stage because the overall information gain and,
hence, the effectiveness of the sensor network should be assessed as a whole.
Despite the information gain varies for different locations, information gains from
data collected at some locations can compensate for those at other locations [89].
The vast majority of optimal sensor placement methods that can be found in the
literature have assumed that all sensors perform without any failure. However, this
assumption is not realistic and may result in the design of a sensor network that
performs poorly when the network is partially impaired (e.g. a sensor fails).
In the above context, Jung and Kim [89] proposed a leak/burst detection approach
similar to that proposed by Hagos et al. [52] but that builds on that work by (1) using
the NSGA-II for multi-objective optimal sensor placement and, most significantly,
by (2) introducing a further criterion in that optimisation, namely, the maximisation
of the robustness of a sensor network given a predefined number of sensors. The
authors defined the sensor network’s robustness as its ability to consistently provide
quality data in the event of sensor failure. Individual sensor failures were considered
in that study, and a coefficient of variation of the rate of correct detections in the
event of a sensor failure was used to assess the variation in performances of the
subsets of a given set of sensors. The authors tested this method on the same
synthetic network used in Hagos et al. [52], performed experiments with very similar
settings and considered a similar number of pressure and flow sensors to be independently deployed. By accounting for robustness of a sensor network, quite
different sensor placements were proposed, thereby confirming that a sensor network’s robustness should be considered at the sensor network’s design stage.
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
43