sensor placements if these factors are not carefully accounted for in the sensor
placement methodologies. On the other hand, measurements uncertainties and
uncertainties related to the leak/burst size have a much lesser impact on the optimal
sensor placements. Having said this, it is envisaged that the influence of other model
uncertainties such as uncertain pipe friction factors and other model-reality divergences on optimal sensor locations should be explored further.
With specific focus on the methods used to solve the optimal sensor placement
problem, it can be observed that the optimal sensor placement problem has been often
solved through optimisation. Complete enumeration techniques (see – e.g. [30]) or
semi-exhaustive search routines utilising a lazy evaluation mechanisms to reduce the
computational cost (see – e.g. [39]) have clearly shown not to scale up well as the
number of sensors to be deployed, the size of the studied network and the complexity
of the problem formulation, among the others, increase. GA, on the other hand, has
shown the potential to be efficiently used to solve carefully formulated problems in
small- to medium-sized networks. They have also been shown to outperform algorithms such as PSO in terms of quality of the sensor placements obtained (see
[74, 75]). However, given the extremely large solution spaces which are typically
present when real-life networks and assumptions are considered, their limitations in
terms of the computational time required to obtain a solution (by not just exploring a
small part of the total solution space) have also been highlighted by several
researchers (see – e.g. [74, 75, 78]). Notwithstanding the fact that several researchers
have attempted to reduce the size of the solution spaces/complexity of the optimisation problem using clustering techniques (as discussed in further detail below), it is
envisaged that experiments with other, potentially more efficient, optimisation techniques should be carried out. Having said this, another relevant issue highlighted by
researchers is the need for accurate tuning of the GA algorithms’ parameters. Therefore, the use of algorithms that are able to automatically adjust their hyper-parameters
such as the hybrid GALAXY multi-objective evolutionary algorithm used by
Boatwright et al. [63] may be beneficial. In addition to all this, investigations into
the possibility of using parallel computing in a multi-core processor framework and
high performance computing should be carried out to effectively enable considering
real-life networks and assumptions and obtain a solution in a reasonable (bearing in
mind that identifying an optimal sensor placement is a task that, in general, needs to
be only carried out at the sensor network’s design stage) time.
With regard to the use of clustering algorithms and as briefly anticipated in the
previous paragraph, several researchers have experimented with such methods with
the aim to reduce the size of the solution space/complexity of the optimisation
problem (e.g. [44, 73]). Valuable contributions were demonstrated in this respect,
and, thus, further investigations into the potential of such methods to help solve the
optimal sensor placement problem in an efficient and effective way should be carried
out. Bearing this in mind, clustering algorithms have also been proposed by some
researchers (e.g. [57, 59]) for solving the sensor placement problem on their own
(i.e. without coupling clustering algorithms with GAs/semi-exhaustive search routines/etc.). Such an approach has been criticised by Sarrate et al. [44, 47] who argue
that it may lead to suboptimal results. However, it should not be possible to
52
M. Romano
placement methodologies. On the other hand, measurements uncertainties and
uncertainties related to the leak/burst size have a much lesser impact on the optimal
sensor placements. Having said this, it is envisaged that the influence of other model
uncertainties such as uncertain pipe friction factors and other model-reality divergences on optimal sensor locations should be explored further.
With specific focus on the methods used to solve the optimal sensor placement
problem, it can be observed that the optimal sensor placement problem has been often
solved through optimisation. Complete enumeration techniques (see – e.g. [30]) or
semi-exhaustive search routines utilising a lazy evaluation mechanisms to reduce the
computational cost (see – e.g. [39]) have clearly shown not to scale up well as the
number of sensors to be deployed, the size of the studied network and the complexity
of the problem formulation, among the others, increase. GA, on the other hand, has
shown the potential to be efficiently used to solve carefully formulated problems in
small- to medium-sized networks. They have also been shown to outperform algorithms such as PSO in terms of quality of the sensor placements obtained (see
[74, 75]). However, given the extremely large solution spaces which are typically
present when real-life networks and assumptions are considered, their limitations in
terms of the computational time required to obtain a solution (by not just exploring a
small part of the total solution space) have also been highlighted by several
researchers (see – e.g. [74, 75, 78]). Notwithstanding the fact that several researchers
have attempted to reduce the size of the solution spaces/complexity of the optimisation problem using clustering techniques (as discussed in further detail below), it is
envisaged that experiments with other, potentially more efficient, optimisation techniques should be carried out. Having said this, another relevant issue highlighted by
researchers is the need for accurate tuning of the GA algorithms’ parameters. Therefore, the use of algorithms that are able to automatically adjust their hyper-parameters
such as the hybrid GALAXY multi-objective evolutionary algorithm used by
Boatwright et al. [63] may be beneficial. In addition to all this, investigations into
the possibility of using parallel computing in a multi-core processor framework and
high performance computing should be carried out to effectively enable considering
real-life networks and assumptions and obtain a solution in a reasonable (bearing in
mind that identifying an optimal sensor placement is a task that, in general, needs to
be only carried out at the sensor network’s design stage) time.
With regard to the use of clustering algorithms and as briefly anticipated in the
previous paragraph, several researchers have experimented with such methods with
the aim to reduce the size of the solution space/complexity of the optimisation
problem (e.g. [44, 73]). Valuable contributions were demonstrated in this respect,
and, thus, further investigations into the potential of such methods to help solve the
optimal sensor placement problem in an efficient and effective way should be carried
out. Bearing this in mind, clustering algorithms have also been proposed by some
researchers (e.g. [57, 59]) for solving the sensor placement problem on their own
(i.e. without coupling clustering algorithms with GAs/semi-exhaustive search routines/etc.). Such an approach has been criticised by Sarrate et al. [44, 47] who argue
that it may lead to suboptimal results. However, it should not be possible to
52
M. Romano