# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 7 Research Trends and Directions  
The reviewed works encompass a wide range of topics regarding quality of wireless visual sensor networks applications, proposing solutions for different contexts. However, some issues are still open and should receive more attention in future works. Such issues are discussed as follows:  
* _Dependable quality:_ only a few papers were found in the literature approaching dependability assessment of visual networks. This is an issue that requires attention in some scenarios, notably due to the particularities of wireless visual sensor networks, demanding proper treatment. For this purpose, dependability must be assessed in terms of quantitative metrics, which is commonly performed by reliability and availability assessment procedures. However, new quantitative metrics could be proposed, especially if they could describe aspects of safety, confidentiality, integrity and maintainability. Moreover, the proposed metrics to assess dependable quality should explore the wide range of possible failures that may affect such networks, potentially increasing complexity. In this sense, research works should be developed proposing methodologies and frameworks to evaluate WVSN in terms of these metrics. They should be assessed integrating aspects that affect the quality of visual monitoring directly, such as occlusion or weather factors. On the other hand, indirect aspects that affect the network operation should also be modeled, such as path loss due shadowing, reflection, refraction, diffraction, hardware failures, and common cause failures, which may impact the execution of proper monitoring functions;
* _Occlusion in target and barrier coverage applications:_ occlusion is probably the main issue that jeopardizes coverage efficiency of an application, reducing the potential of visual information that can be retrieved from the visual nodes. This issue has been well addressed in the literature regarding area coverage applications, due to its evident effect. However, it is not commonly discussed for target and barrier coverage applications. It is necessary to model obstacles and compute the resulting occluded field of view of the sensor nodes, in order to properly determine the application coverage and to assess its quality, especially its dependable quality. That way, it is necessary to spatially model and georeference the possible obstacles in an application (cars, trees, objects, people, etc. ), as well as the targets or the built barrier, and the camera's Field of View. These models must be overlapped to identify the intersection among them, which is the occluded area. Then, a region computation algorithm must be executed in order to map the region outside of such intersection, aiming to identifying the amount of covered targets or the extension of the covered barrier; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Works** & **Advantages** & **Disadvantages** \\ \hline Region of interest & [79] & \begin{tabular}{c} Manage prioritization of \\ the application \\ \end{tabular} & \begin{tabular}{c} Require a pre-processing \\ analysis to define the \\ regions of interest \\ \end{tabular} \\ \hline Ellipse fit root & [80,81] & \begin{tabular}{c} Accurate metric, fittable \\ to 3D applications \\ \end{tabular} & \begin{tabular}{c} Requires a specific target \\ characterization \\ \end{tabular} \\ \hline Occlusion rate & [82] & \begin{tabular}{c} Provides a guarantee of a \\ minimum target \\ coverage \\ \end{tabular} & \begin{tabular}{c} Neglects resolution and \\ sharpness of the covered \\ target \\ \end{tabular} \\ \hline Frame rate; Pixels & [83,84] & \
\begin{tabular}{c} Optimize available \\ resources while perform \\ high resolution \\ monitoring \\ \end{tabular} &
\begin{tabular}{c} May impose a high \\ throughput to a strongly \\ connected network \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}