## 1 Introduction  
Industrial applications have become more critical and complex over the past years, mainly due the intensification of processes automation and their distributed operation [1]. Many of those applications comprise one or more Wireless Visual Sensor Networks (WVSN), aiming at multi-sensory monitoring for tasks as quality inspection, surveillance and system guidance. Whatever is the nature of such sensors-based networks, their failures can be dangerous to people, lead to environmental damages or even result in economic losses, pushing WVSN applications to pursue a sufficient dependability level [2]. In general, the concept of dependability concerning scalar Wireless Sensor Networks (WSN) has addressed hardware and connectivity failures when modelling faulty conditions [3]. However, for a visual monitoring context, a new type of failure must be also considered, since the way cameras sense the environment is subject to a whole new group of errors and impairments [4]. In fact, visual coverage failures are resulted from total or partial lack of visual information retrieved from cameras, with different particularities depending on the way the environment is perceived. Therefore, when combining hardware, connectivity and coverage failures, more comprehensive dependability evaluations can be performed, potentially benefiting the management of critical sensing applications. When assessing the dependability of wireless visual sensor networks in industrial environments, in a broader perspective, the concept of Quality of Monitoring (QoM) can be defined. The QoM refersto the capability of a network to perform the expected monitoring functions over a region of interest [5]. In other words, it is not only considered the amount of retrieved visual information when assessing quality, but also definition and sharpness parameters [6]. In practical means, any application may have a minimum expected level of QoM, changing the perception of coverage failures when evaluating dependability: a coverage failure will happen when an application fails to fulfill its expected minimum QoM level. Several aspects can affect the normal operation of cameras. Common examples are dynamic external (outdoor) factors such as (high/low) luminosity, fog, rain, snow and dust. Particularly for indoor industrial areas, however, there will be two main causes of visual failures that will affect the retrieved visual data and, consequently, the achieved dependability of an application: (1) occlusion and (2) distance of view. Occlusion will be produced by some static or mobile obstacle, potentially reducing a camera's Field of View (FoV) and indirectly impacting the overall monitoring quality. In a different way, the distance of view is associated to the sharpness of the retrieved visual data [7]. Both types of impairments may simultaneously affect visual sensor nodes and thus they will be modeled as the main causes for QoM degradation. This article proposes a methodology to allow dependability evaluations of wireless visual sensor networks under the effect of different types of failures. For that, we integrate some previous contributions into a single unified mathematical model, incorporating new elements to better encompass the challenges of industrial monitoring applications. Actually, the proposed methodology comprises common hardware and networking failures associated to scalar sensors, as well as visual coverage failures generated by occlusion or low monitoring quality (modelled as the distance of view). Doing so, researchers acquire a unified model that comprehensively cover frequent but complex failure conditions in such scenarios. Additionally, extensive discussions about practical definitions of the model's parameters are conducted in this article, addressing an important researching gap that has not been properly considered by the literature. For this purpose, an example of a paper mill application is considered for simulations (Iggesund Paperboard [8]), taking as reference the WirelessHART protocol to connect the sensors [9].