## 4 Proposed Methodology  
The dependability assessment methodology proposed in this article is aimed at solving an important quality evaluation gap in critical applications that employ scalar and visual sensors to  
Figure 4: Quality of the performed area coverage when viewing monitoring blocks. perform monitoring tasks. For that, we integrate and unify some previous methodologies [5; 10; 11], achieving a more complete mathematical solution for such scenarios. In this way, the proposed methodology for dependability evaluation considers different visual coverage failures, which occur when the required area coverage or quality of monitoring level are not fulfilled, either by an incorrect deployment or by occlusion. The flowchart of the proposed evaluation methodology is presented in Figure 5 and depicts the methodology in four stages: _coverage analysis_, _connectivity analysis_, _Fault Tree generation_ and _dependability analysis_. Backwards, the _dependability analysis_ (the last stage) is performed in terms of its quantitative attributes (reliability and availability), as it is commonly addressed in the literature [23; 24; 25; 26]. For this task, the SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) tool [27] is used to numerically compute a hierarchical model of a fault tree embedded with Markov chains. It is worthy to remark that it is an automated methodology, which means that, besides the input data at the initial stage of the methodology, no further interaction with the user is required for dependability assessment. The hierarchical model is built in the previous stage of the methodology, the _Fault Tree generation_. For this, the proposed methodology assumes a network that is organized in a tree topology, rooted at the gateway node. This node manages the system monitoring application, receiving data from the other visual nodes. Therefore, a visual application will fail if the network is not capable to deliver the required visual information to the gateway. This can happen due to failures of network nodes that compose the _Network Failure Condition_ (NFC), which is a logical expression that indicates which nodes or combination of nodes (\(fc\_Comb_{i}\)) must fail to lead to the application failure. The nodes participating in the NFC are called _essential nodes_. The NFC is used to build a fault tree. In this case, an application failure is identified whether the NFC is assessed equals to _true_, which is equivalent to the TOP event of the fault tree be assessed equals to _true_. Figure 6 shows the gates of the fault tree structure considered in the methodology. Figure 5: Flowchart of the proposed evaluation methodology. Figure 6: Fault Tree models of (**a**) network, (**b**) combinations, (**c**) paths and (**d**) devices [10]. A combination of nodes (devices) fails if all paths (\(fc\_Path_{i}\)) that connect the gateway to each node in the combination fail. A path fails if any link (\(lk_{i}\)) or node (\(fc\_Dev_{i}\)) in the path from the node to gateway fails. Finally, a node fails if its hardware (\(hw\)) fails or if its battery discharges (\(bt\)) [10]. The basic events \(lk_{i}\), \(hw\) and \(bt\) must be assigned to battery, hardware and link dependability functions (reliability or availability functions, according to the evaluation interest), resulting from the respective Markov chain evaluation, according to Figure 7. These Markov chains describe the component behavior through two states: \(UP\) (operational) and \(DOWN\) (failed). Transitions between these two Markov chain states are described by the failure (\(\lambda_{hw}\), \(\lambda_{lk}\)) and repair rates (\(\mu_{hw}\), \(\mu_{lk}\), \(\mu_{bt}\)), respectively. In case of battery, the states \(B_{0},B_{1},\ldots,B_{n-1}\) represent progressive discharging states, the state \(B_{n}\) represents a fully discharge battery and the transition rates \(\lambda_{btk}\), \(k=0,\ldots,n-1\), are the battery discharge rates in function of the power consumption.