Indeed, elements of research conducted for other sampling design purposes are
conceptually applicable to frameworks aimed specifically at optimal sampling
design for leak/burst detection and localisation. Most notably, the optimisation
approaches and algorithms developed for (or merely implemented in) the wider
sampling design literature are distinct from the objectives to which they are applied.
In addition to all of the above, it is also worth highlighting the fact that with the
rise of easy-to-use and low-cost sensing devices, Internet of Things (IoT) technologies and edge analytics an increase in the density of heterogeneous sensors
deployed in WDSs may be expected in the near future. In this scenario, pressure
and flow devices will be part of a much wider network of sensors. Therefore,
considerations regarding issues that have been the focus of research in the broader
wireless sensor networks’ literature, such as reliable communications, efficient
routing protocols, power management and computation/communication overhead,
to mention just a few, will need to be accounted for when developing optimal sensor
placement techniques for leak/burst detection and localisation in WDSs [99].
Finally, it must be noted that in this review the requirements for additional
instrumentation have been looked at in the context of WDSs subdivided in DMAs.
The rationale for this is that many of the optimal sampling design techniques for
leak/burst detection and localisation found in the literature have been developed and
tested under the “DMAs existence” assumption. This, in turn, is probably due to the
fact that DMAs are seen as ground zero data-wise on the water companies’ journey
towards operating smart water networks [100]. As evidenced by the fact that, over
the past two decades, a number of technology vendors have aligned their business
models based on the data flows captured or technologies required by a DMA
approach, many water consultancies have implemented their DMA-dependent
water balance methodologies around the world, and major industrial players have
tailored products for managing sectorised networks [100]. Having said all this,
however, the “DMA model” (led by UK water companies) only proliferates in
Europe while gradually been adopted in countries such as Singapore, Chile, Brazil
and Australia. The “non-DMA model”, on the other hand, represents the bulk of the
world’s WDSs, and it proliferates in the USA as well as Germany and most of the
developing world [100]. From the “non-DMA model” perspective, it may not be
cost-effective or even practical to subdivide a WDS into DMAs. Therefore, the
development of further optimal sampling design techniques for leak/burst detection
and localisation should bear the global situation in mind and be pursued independently by the existence (or otherwise) of DMAs, which does not seem to be a strict
requirement for this task. Furthermore, it is worth mentioning that this task may
perhaps also be facilitated by the fact that, in recent years, the Virtual DMAs
(V-DMAs) concept has started to come to fruition as data from a suite of technologies such as insertion, ultrasonic and acoustic flow metering and smart customer
meters has started to be leveraged on a larger scale.
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
57
conceptually applicable to frameworks aimed specifically at optimal sampling
design for leak/burst detection and localisation. Most notably, the optimisation
approaches and algorithms developed for (or merely implemented in) the wider
sampling design literature are distinct from the objectives to which they are applied.
In addition to all of the above, it is also worth highlighting the fact that with the
rise of easy-to-use and low-cost sensing devices, Internet of Things (IoT) technologies and edge analytics an increase in the density of heterogeneous sensors
deployed in WDSs may be expected in the near future. In this scenario, pressure
and flow devices will be part of a much wider network of sensors. Therefore,
considerations regarding issues that have been the focus of research in the broader
wireless sensor networks’ literature, such as reliable communications, efficient
routing protocols, power management and computation/communication overhead,
to mention just a few, will need to be accounted for when developing optimal sensor
placement techniques for leak/burst detection and localisation in WDSs [99].
Finally, it must be noted that in this review the requirements for additional
instrumentation have been looked at in the context of WDSs subdivided in DMAs.
The rationale for this is that many of the optimal sampling design techniques for
leak/burst detection and localisation found in the literature have been developed and
tested under the “DMAs existence” assumption. This, in turn, is probably due to the
fact that DMAs are seen as ground zero data-wise on the water companies’ journey
towards operating smart water networks [100]. As evidenced by the fact that, over
the past two decades, a number of technology vendors have aligned their business
models based on the data flows captured or technologies required by a DMA
approach, many water consultancies have implemented their DMA-dependent
water balance methodologies around the world, and major industrial players have
tailored products for managing sectorised networks [100]. Having said all this,
however, the “DMA model” (led by UK water companies) only proliferates in
Europe while gradually been adopted in countries such as Singapore, Chile, Brazil
and Australia. The “non-DMA model”, on the other hand, represents the bulk of the
world’s WDSs, and it proliferates in the USA as well as Germany and most of the
developing world [100]. From the “non-DMA model” perspective, it may not be
cost-effective or even practical to subdivide a WDS into DMAs. Therefore, the
development of further optimal sampling design techniques for leak/burst detection
and localisation should bear the global situation in mind and be pursued independently by the existence (or otherwise) of DMAs, which does not seem to be a strict
requirement for this task. Furthermore, it is worth mentioning that this task may
perhaps also be facilitated by the fact that, in recent years, the Virtual DMAs
(V-DMAs) concept has started to come to fruition as data from a suite of technologies such as insertion, ultrasonic and acoustic flow metering and smart customer
meters has started to be leveraged on a larger scale.
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
57