# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 2 Related Works  
Due to its importance, dependability evaluation is an issue widely addressed in the literature, especially on WSNs. Several papers can be founded surveying some aspect of dependability on WSNs [10; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25], while some others papers survey WVSNs [26; 27; 28; 29; 30; 31] as their main research areas. However, just a few works have discussed dependability aspects of WVSNs, with emphasis on the availability assessment [14; 32; 33; 34; 35]. [PERSON] et al. [36] analytically evaluates dependability issues in terms of reliability and _producibility_ (a new attribute introduced in that work), modeling the behavior of sensor nodes using Markov Reward models. The energy consumption is computed considering that a sensor can be in active or sleep state, and the battery charge is estimated in order to detect a failure. his information is used to evaluate reliability and _producibility_ of each sensor node, and an algorithm is proposed to evaluate WSNs' dependability, taking into account the network topology and the sensor redundancy. New concepts related to dependability are also proposed by [PERSON] et al. [37]. In that paper, an approach is presented to quantify reliability and availability, in addition to maintainability, safety and integrity. These dependability attributes are evaluated based on metrics such as mean time to reliability failure (MTTRF), mean time to availability failure (MTTAF), mean time to maintainability failure (MTTMF), mean time to safety failure (MTTSF) and mean time to integrity failure (MTTF). [PERSON] [38] uses Stochastic Activity Networks (SAN) to model time-continuous systems, including maintenance and fault diagnosis aspects. Moreover, through Monte Carlo Simulation, the authors also assess dependability attributes such reliability and availability. For this purpose, it is shown how to model sensors faults, measurement noise, and how to use these data to evaluate dependability. [PERSON] et al. [39] consider the Internet of Things context to evaluate dependability, although, according to the authors, their approach can be adapted to evaluate more complex scenarios, assuming any routing strategy and topology types. Systems are modeled by Markov Chains, which are evaluated using the SHARPE tool. These models consider node redundancy and the mean time to failure (MTTF) of the system, metrics that can be used to assess reliability and availability. Since analytical modeling is a time-consuming task, demanding much effort for complex topologies, some authors propose automated model generation approaches [6; 8; 12; 40; 41]. [PERSON] et al. [12] propose a framework to assess both dependability and performance of WSNs through automatic generation of analytical models. The proposed modeling approach considers unreliable devices and unreliable links, power consumption, routing protocols, workload and radio specifications. The proposed framework integrates behavioral models (analyzed by the AVRORA simulator [42]) and analytical models (described by SAN formalism). In addition, the proposed framework is capable of inferring a realistic WSN model and to evaluate this model with respect to connection resiliency, coverage level of the monitored area, data delivery resiliency and efficiency, availability, lifetime and isolation time of nodes. Nevertheless, the model templates must be predefined _una-tantum_ by a domain expert. [PERSON] et al. [6] propose a methodology for automatic generation of analytical dependability models based on FTA, in industrial environments that are subject to permanent faults on network devices. The methodology is integrated with the SHARPE tool and evaluates the application behavior with regard to reliability, unreliability, availability, unavailability, MTTF and component importance (Birnbaum and Criticality), considering line, star, cluster and mesh topologies. [PERSON] et al.