Hypersensing. Wireless data acquisition in demanding environments.

In recent times, SCADA platforms have been the systems that have controlled and managed most of the important infrastructures, including those for the distribution and supply of public or critical goods such as water of all kinds, energy, gas, or coverage stations.

Even so, as platforms they were initially developed to control ‘internal’ systems, understood as processes or control networks located under a properly acclimatized installation, where the different elements and devices were wired and powered. Likewise, the so-called telemetry also referred to the control of more or less protected sites, yes, contemplating scenarios of poor communication and without personnel close to the installation.

In both scenarios, it was rarely contemplated to monitor or add information to the SCADA from sensors located outside or in demanding locations, either due to the nature of the place itself or its difficult access.

The reason for this is simple, having this information used to have a poor return, either due to the difficulties of wiring – and protecting said wiring – to the indicated place both the power and the communications.

On the other hand, the alternative of installing sensors or actuators that worked with batteries was also not viable. Whether due to how the solutions or communication technologies of the time worked, it implied a continuous replacement of batteries to be able to have the system working. To all this, currently one more obstacle must be added, cybersecurity. That a wireless communication is secure is not optional.

With the revolution that the new IoT paradigm has brought, new technologies and new working methods have burst in strongly, finally solving the gap. However, putting your feet on the ground a bit, a solution cannot be considered complete if it does not in turn have a good encapsulation that allows it to function for long periods of time in environments of this type. That means not only waterproof encapsulations, but also resistant to corrosion or high pressures, depending on the use.

With these solutions, added to the improvements also in real-time management systems, scenarios that were previously unresolvable are opened. A clear example may be the improvement of pressures in water management. To make a comparison, electrical companies have been working preventively for years on how much energy they should provide at each moment of the day and on each day of the year.

That has been possible because for years it has been possible to monitor and historicize the energy consumption of the electrical park of the territory, this fact, added to the preventive analyses, allows to make approximations very close to the reality about the future consumption. As if that were not enough, the loop closes with a real-time reading that estimates the amount of electrical energy that must be produced to finish squaring the small differences.

In the world of water management there is a similar problem, still unresolved, the pressures. Today, operators do not know the different pressures that are necessary in the network, that is, for each consumption node. To solve it, historically it has been chosen to oversize the pressure exerted, with the consequent maintenance problems and high consumption that this causes.

Thus, that problem can be solved today. A constant deployment of wireless pressure gauges that would allow in the short term to do a real-time management and in the long term a predictive management.

From here, the fields of application are infinite, from detection sensors of presence in unattended installations, water quality along its entire route, humidity sensors located outdoors, level of residual water in sewers or wells, gas leaks or even smoke detectors.

Once this phase has been overcome, it will be possible to study implementing ways to exploit this information with the different tools that are emerging. Doing it before may involve a large investment in solutions with great calculation capabilities, but, they will not be able to extract accurate or complete conclusions since they will not have a good reliable source of data.