Improving human safety and security in all aspects of everyday life is one of the most important and challenging topics for the international research and industrial communities.

In such a context, one of the applications of main interest is represented by the structural health monitoring (SHM) of complex and large structures such as skyscrapers, bridges, aircrafts, railway systems, wind energy generators.

In this framework, one of the available and general‐purpose technologies that can be proficiently exploited in the implementation of new‐generation SHM systems is represented by wireless sensor networks (WSNs). 

As a matter of fact, WSNs proved to be a suitable tool for gathering data during long periods (thanks to the low power consumption) and in a distributed and pervasive manner, without the need of a specific and continuous maintenance, thus with limited costs.

Because of the huge amount of information collectable by a WSN or analogous distributed sensing technologies, efficient inversion tools are needed to detect changes, cracks or anomalies in the structure under test (SUT) in a time‐efficient and reliable fashion. The information on the status of the SUT should be available to the final decision‐makers/end‐users in real‐time or almost real‐time.

Towards this end, two main issues have to be carefully addressed:

  1. the development of fast methods that provide the SUT descriptors in real‐time;
  2. a method to infer the reliability of the estimated values of the SUT descriptors by exploiting the collected information and the a‐priori information on the problem at hand.

Unfortunately, the development of algorithms complying with the above requirements is an extremely challenging task, and no general-purpose solution has been proposed in the state-of-the-art to achieve these goals in the SIRENA reference scenarios.