## 1 Introduction  
Monitoring of slopes that are potentially involved in gravity-induced deformations has become a relevant topic in the last decades. The challenge is generally related to the transmission of alarm signals for early warning in the case of exposed infrastructures (such as railway, highways, pipelines and aquducts) or natural and cultural heritage, as typically no significant deformations are detectable before failure occurrence. The recognition of failure precursors by multi-parametric monitoring systems, which is a sensor network able to monitor several kinds of parameters, in natural contexts as well as of the forcing actions that generate deformations is an important target in addressing this challenge. It is well known that changes in the stress conditions of rock masses due to impulsive or viscous deformations can cause rapid and violent failures able to widely affect slopes and generate landslides [1]. Furthermore, the ongoing deformational processes, which lead to failures of rock masses, represent a complex geomechanical topic because the jointing conditions, as well as the effective stress field, can strongly constrain precursors, such as microstrains or microcracks, which are also associated with acoustic or vibrational emissions [2; 3; 4; 5; 6; 7]. Multi-parametric monitoring systems linked to early-warning procedures appear to be a very good solution for use in prevention plans and risk mitigation. Multi-parametric monitoring offers a temporary strategy to ensure public safety prior to implementing more definitive vulnerability reduction actions. Due to the recent developments in the computer science disciplines, monitoring system can benefit from the concept of cloud monitoring [8], which is devoted to early warning that requires huge data sets to be managed in very short time by (semi)automatic data flow-to-data processing analyses. The main peculiarities of cloud parametric monitoring systems (CPMS) are: (i) redundancy of the measurement data; (ii) secondary relevance of the a priori understanding of cause-to-effect relations of the monitored phenomena; (iii) reconstruction of the ex-post relations between destabilising causes and/or forcing factors and induced effects; and (iv) quantification of the alert levels and connected control index for infrastructure management. In this way, CPMS can be designed as part of a wireless sensors network (WSN) for real-time data transmission [9; 10]. The setup of a multi-parametric monitoring system for an identified landslide hazard requires the identification of the most relevant factors with respect to the physical mechanisms, which potentially lead to slope failure to classify them into a hierarchy according to their role, impact, chronology and causality effects relative to the considered phenomenon [11]. [PERSON] et al. [12] divided these factors, which contribute to making a rock mass prone-to-failure, into three different categories: (i) _predisposition factors_ are characteristic of the typology of the slope. Some slopes are, in fact, more susceptible than others to rock fall activity due to background factors, such as topography, vegetation, lithological parameters, and fracture geometry and density; (ii) _preparatory factors_ are the cause of medium- to long-term changes in resistance. For monitoring to be efficient, their small and almost imperceptible effects must accumulate until rupture; (iii) _trigger factors_ change over the short- to medium time frame and constitute the most direct causes of failure. Unlike _preparatory_ and _trigger factors_, which are defined by their action period, _predisposition factors_ do not exhibit any evolution over time. Detecting these factors before failure occurrence presents a challenge in multi-parametric system design [13]. Furthermore, these factors are strictly connected to the assessment and prediction of landslide hazards in the framework of a multi-parametric scenario.