## 4 Proposed Methodology
At the _connectivity analysis_ step (the second stage in Figure 5), the paths from the nodes of NFC to the gateway are represented in the fault tree, and they are defined according to the routing strategy. For this, it is necessary to know the NFC of the considered network. At this point, we extended our previous work presented in Reference [10]. In that work, the NFC was generated based on the covered area. However, as important as the amount of visual information, the quality of the gathered information is crucial for applications. Actually, both the amount and quality of this information can be jeopardized by occlusion, leading to a dependability decreasing. That way, these coverage failures in the considered network have to be properly processed. In fact, coverage failures may happen in different ways and in different moments of the network lifetime. Nevertheless, in order to properly evaluate the dependability of the applications, the proposed methodology processes all coverage failures at once, achieving a final dependability perception. Therefore, the standard procedure is to sequentially compute all modelled coverage failures, first computing occlusions on all visual sensors and then processing the QoM associated to the Depth of View based on Occluded FoV (OFoV). The computation of the OFoV will consider all static or moving obstacles that can produce some level of occlusion on the deployed visual sensors. For that, the formulations in Reference [11] can be leveraged to achieve a set of Occluded FoV for the considered network, reflecting the impacts of the obstacles along the time. Then, the QoM of that set of active visual sensors can be also processed based on the definitions in Reference [5], but now assuming that the FoV of any visual sensors may be not an isosceles triangle. In other words, two sequential layers of coverage failures will be computed for the visual sensors, and their resulted coverage on the monitored field will already reflect the impacts of both types of coverage failures. This general processing principle is depicted in Figure 8.  
Figure 7: Markov Chain models of (**a**) hardware, (**b**) link and (**c**) battery [10]. As an important remark in Figure 8, while a visual sensor may be not occluded by an obstacle, which may result in an OFoV similar to the original FoV, the QoM-aware FoV of that sensor will be always computed. Based on the processing of these coverage failures, we define the NFC considering that the application will fail if a combination of nodes are not able to cover a minimum amount of covered area, a minimum quality of monitoring and also considering the minimum percentage of FoV of each visual sensor. For this last case, it is considered that, if a single sensor node cannot cover its minimum FoV, its camera is turned off, and the node operates as a _relay node_, that is, a node that only re-transmits messages, therefore consuming less power, saving battery, extending its lifetime, and so increasing the system dependability. Initially, the methodology requires some supplementary data from the user in order to characterize the network and the application requirements. This information is related to network configuration (e.g., topology), visual coverage attributes, nodes, the evaluation process itself and specially the application requirements, which is the minimum visual coverage percentage (\(CA_{min}\)), the minimum QoM (\(AQM_{min}\)) and the minimum FoV (\(FoV_{min}\)). The Algorithm 1 describes the redesigned NFC generation, when a combination of sensor nodes is included into the NFC expression if it provides the required area coverage and QoM (Line 33). Doing so, the dependability evaluation can be carried out considering the mathematical parameters of the scenario. The Algorithm 1 starts computing the OFoV of each visual sensor according to [PERSON] et al. [11] (Line 2). Then, it is verified which monitoring blocks are covered by each visual sensor and their respective QoM.