## 1 Introduction
In such a way, multiple devices can monitor the four IEQ domains simultaneously and continuously in indoor environments, with recorded data accessible to occupants through dedicated dashboards [23; 24; 25]. Among the several monitoring devices proposed thus far, a multi-sensor capable of providing a real-time feedback on measured IEQ and displaying it was proposed in [24]. In this study, data from on-board sensors were collected, and averages of each monitored physical quantity updated every 10 min. In [26], a device combining low-cost IEQ sensors with a survey interface to gather feedback regarding the occupants' perceived indoor environmental comfort (IEC) was presented. While it allowed data transmission through a 3G modem, details about the cloud platform were not provided. The SAMBA device proposed in [23] allowed for a continuous IEQ monitoring in indoors. Each monitoring device acquired sensor data during a 270 s window sampling, followed by a 30 s data transmission window, during which averages of sampled data are sent to a cloud server. Another monitoring device, ENVIRA, was proposed in [27], where relevant IEQ parameters are integrated into an overall index through individual weighting coefficients. Finally, a system based on intelligent edge computing was proposed in [28], in which the developed measuring device collects data from 14 types of environmental sensors, and indoor air quality is predicted. The development of such monitoring devices deals with the addressing of several challenges simultaneously. One primary concern arises from the use of low-cost sensors, as their readings often deviate from those of reference instruments [29]. It is, therefore,required to conduct a metrological characterization of the low-cost sensors employed into the developed multi-sensor unit, as discussed in [30]. Additionally, attention should be given to the placement of sensors within the monitoring device to prevent mutual interference. Lastly, there is a need to enhanced data processing capability at the monitoring device level. In addition to monitoring devices, the design of a complete IEQ monitoring system also includes the development of the cloud platform and of the dashboarding application. The design challenges faced in this regard involve efficient data storage and retrieval, as well as the creation of user-friendly and highly customizable user interfaces, aspects that are not typically exhaustively discussed in the architecture of such platforms [31; 32]. Regarding the visualization of sensor data graphs, the approaches used in the literature have been essentially twofold: leveraging dashboarding environments or developing a dedicated application/website. In research environments such as [27; 31; 32], the preferred solution often learns towards dashboarding environments. These environments allow for customization without the need for code development. Conversely, in commercial-like products such as [33] or widely adopted platforms such as [22], the adoption of dedicated applications offers superior usability, service performance, and scalability. However, the limitation of the former approach lies in the limited possibilities for user interface customization. Conversely, the latter case necessitates reliance on IT professionals for both initial development and subsequent modifications. In summary, several aspects regarding the design of a complete IEQ monitoring system have not been addressed thoroughly in existing works. More precisely, data processing at the monitoring device level typically involves only averaging raw data from sensors over fixed time intervals, which may prevent the detection of rapid variations in monitored physical parameters. On the server side, the actual database organization is neglected, as well as effective solutions for rapid data retrieving. Additionally, proposed solutions for data visualization lack of flexibility and ease of modification for non-IT professionals.