IOT enviromental quality monitoring in smart buildings in presence of measurement uncertainty: a decision making approach

The change of living concept from "traditional" to "smart" concerns how we live and relate in spaces that today, more than ever, are "sensitive" (i.e. spaces where digital technologies occupy a prominent place in the monitoring and control of buildings, with the aim of achieving high levels of quality of life). We are therefore witnessing the creation of new declinations of living and, in this context, the internet of things (IoT) represents the starting point for the creation of connected products that "share" the information they detect with other objects or people on the network. In this scenario, the authors propose an original approach to measurements for the assessment of comfort in living environments. The work consists in the design and implementation of a measurement station, which acquires and analyses data collected by the network of distributed sensors and activates forced ventilation if the level of comfort is below the desired threshold. In such situations where measurement data are compared with a threshold value, it is necessary to consider how measurement uncertainty affects the decision taken; in this particular context, since the activation of actuators involves energy consumption, the decision on the effective threshold crossing should be well thought. For this reason, the aim of this work is to propose a smart monitoring system that through the setup and calibration of two decision-making algorithms, can decide if the measured value is below or over the threshold set with a known probability. In this way, the end user can chose an appropriate strategy, calibrated on the specific living environment, which allows to maximize either environmental comfort or energy saving, depending on the specific needs.