# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 2 Related Works
[40] present a modeling strategy to evaluate the reliability of WSNs, considering the battery level as a key factor, but also considering routing algorithms, unreliable devices and unreliable links. The WSN power consumption is evaluated by Coloured Petri Net (CPN) models that are composed by basic models, which represent the power consumption of small parts of the application or the network. A tool is also proposed to automate the evaluation of WSNs reliability. This work has been extended in [41], in order to support analyses with the power consumption of WSN applications and communication protocols. [PERSON] et al. [8] propose a toolset to support the evaluation of the dependability of WSNs in industrial environments, focusing on the automatic generation of analytical dependability models fromAADL (Architecture Analysis and Design Language) architecture models. The proposed framework is integrated with the SHARPE tool and decides which modeling technique is the most appropriate for each case according to the system structure and dependability metrics. Regarding WVSNs, [PERSON] et al. [32] identify and discuss the main availability issues of this application domain. This paper focuses on how redundancy should be considered to improve the availability level of WVSNs, with respect to a camera's field of view (FoV) overlapping, sensing similarity and sensing relevance. Moreover, the authors discuss common hardware and coverage failures [43] that can affect such availability level. The authors indicate that the availability evaluation in WVSNs has to consider coverage quality, quality of viewing, barrier monitoring, directional k-coverage and users' perceptions. In [32], practical approaches and mechanisms to evaluate and to enhance availability in WVSNs are also discussed, and, in [44], authors discuss routing mechanisms based on the sensing relevancies of source nodes for critical applications in WVSNs. Redundancy in WVSNs is considered in [33; 35], and used as a dependability metric. In these cases, availability is evaluated with respect to the redundancy level. The authors compute the FoV of each visual sensor node in order to know the application FoV and to select the redundant nodes. In [33], this metric is evaluated considering the minimum percentage of FoV and the maximum acceptable angle between sensors' orientation. In [35], occlusion is added as a redundancy parameter, changing the way to compute FoV. Additionally, an algorithm to adjust cameras' orientations is proposed to enhance the availability of WVSNs with occlusion. In [45], the selection of sensor nodes for barrier coverage monitoring is also addressed based on redundancy coverage level of the nodes. In [34], a new coverage metric is proposed, the Effective Target Viewing (ETV), which characterizes the percentage of viewed parts of targets' perimeters. This metric is exploited to assess the availability of WVSN monitoring applications. In this case, ETV is associated with an availability state, which may be \"yes\" (available) or \"no\" (unavailable), according to the defined Minimum acceptable ETV (M-ETV) threshold. However, although addressing dependability issues of WVSNs, the papers [33; 34; 35] only address coverage aspects, ignoring communication and hardware issues. [PERSON] et al. [14] partially address these issues using the methodology proposed by [PERSON] et al. [6] to perform availability assessments in WVSNs. This evaluation is associated with all visual sensors nodes that are monitoring a target, considering hardware failures (battery discharging), communication failures (loss of path to a sink node) and coverage failures (loss of view over targets). However, communication failures are modeled in such a way that did not consider the effects of routing protocols. The network failure condition (NFC) is identified by a voting gate (_k-out-of-N_), meaning that whenever \(k\) out of the \(N\) visual sensors nodes monitoring the target fail, the application will fail, ignoring the different importance degree of sensor nodes.