## 2 Related Works
### Occlusion and QoM when Considering Area Coverage  
These are some important topics that have been considered in different ways when performing visual monitoring or when assessing monitoring quality.  
References [12; 13] addressed full-view area coverage in WVSN, aimed at the computing of the total area that is being covered by the sensors. For that, those works take into account the orientation of each point or region that is faced by the sensors. In these cases, the Quality of Monitoring is directly related with the facing angles.  
The authors of References [14] also addressed area coverage, focused in the automatic determination of the number, position and pan-tilt-zoom setting of a set of cameras to maximize the coverage. In this process, the authors consider QoM by handling the distortion models that can affect the quality and usability of the acquired data.  
For [PERSON] and [PERSON] [15], area coverage is considered in a different class of WVSN, the Unmanned Aerial Vehicle (UAV) networks. In this case, each UAV is equipped with a camera, being responsible to perform a persistent full coverage of the area. If eventually an UAV fails, it is considered \"unrecoverable\" and the network must be redeployed, adjusting position and loitering movements to keep the area covered. An important aspect is that the authors consider the loitering altitude constant in order to keep the coverage quality constant.  
Although they consider QoM in area coverage, neither the works in References [12; 13; 14; 15] actually compute a sensor node's or network's QoM. The work in [PERSON] et al. [6] overcomes this issue by defining a weighted sensing quality, which is calculated over a predefined deployment scheme. In that case, it is discussed the importance of sensing area to establish QoM in a full area coverage scenario. However, several restrictions are imposed to the network regarding to the nodes' position, orientation and viewing angle.  
In Reference [5], QoM is addressed extrapolating several deployment constraints imposed in Reference [6], such as cameras' position, orientation and viewing angle. New QoM-based metrics are proposed, allowing new researches related to optimization, comparison or exploitation of different quality aspects, such as redundancy and dependability.  
Some papers also relate QoM with occlusion, which is commonly addressed as an aspect that reduces the coverage area. In Reference [16], authors do not address the overall area coverage quality, since they consider occlusion to determine a satisfactory sensing quality of a monitoring area, when a set of waypoints are chosen for taking picture of some objects. The proposed coverage technique is used to achieve energy efficiency by minimizing the maximum energy consumption among sensor nodes.  
Still considering this overall scenario, [PERSON] et al. (2017) and [PERSON] et al. (2011) address occlusion in a more significant way, proposing computation of area coverage under occlusion. The work in [PERSON] et al. (2017) proposed a model to compute occlusion caused by simplified static obstacles, which were modeled as 2D walls (lines). In that work, occlusion affected FoV overlapping and selection of redundant nodes, directly impacting on availability assessment. Authors in [PERSON] et al. (2011) provide an enhanced occlusion approach, modelling the obstacles as mobile 2D rectangles, which are a better and reasonable approximation of real objects. The goal of that work was the selection of visual nodes based on effective viewed area to better perform existing optimization or quality assessment approaches.