# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 3 Reviewing Spatial Coverage Quality  
Spatial coverage quality is related to the coverage problem, which is a fundamental issue in sensor networks, reflecting how well a region is monitored. In WVSN, this problem has been addressed as the \"visual coverage\", a quantifiable property of camera-based networks, describing from a pragmatic viewpoint what the application can\"see\". More specifically, visual coverage expresses the capability of a set of visual sensor nodes to represent the real world through the data gathered by their cameras, and thus informing the most essential requirement of any computer vision task [24; 25]. Visual coverage can present different characteristics and requirements according to the objective of coverage. Three objectives are most commonly considered: _target coverage_, _area coverage_ or _barrier coverage_. _Target coverage_ approach is focused on monitoring of a set of targets. The _area coverage_ is related to monitoring of one or more areas of the monitored field. At last, the _barrier coverage_ monitors a conceptual, long and narrow  
Figure 1: Proposed classification and commonly associated coverage aspects. barrier belt area of sensors, aiming at the detection of intruders that attempt to cross the deployed region [26; 27; 28; 29]. Due to their relevance when assessing visual coverage quality, all these objectives are considered when organizing and comparing quality metrics in our proposed classification. Figures 2-4 illustrate WVSN being monitored by the visual sensor nodes \(V_{1}\), \(V_{2}\), \(V_{3}\) and \(V_{4}\), performing target, area and barrier coverage, respectively. The visual sensor nodes are represented by the red circles and their field of views are approximated in this work, without loss of generality, by an isosceles triangle. An application based on _target coverage_ analyzes the visual data looking for specific points, objects or sub-regions called targets, disregarding the remaining elements in the image. Targets can be cars in a parking lot, machinery in an industry, doors in a house, trees in a forest, etc. In Figure 2, the targets are represented by the blue squares \(T_{i},i=1\ldots 9\). The targets within the field of view of a visual sensor are said to be covered by this sensor node, such as the targets \(T_{8}\) and \(T_{9}\) with respect to the visual node \(V_{4}\). A target can be covered by more than one visual node, such as the target \(T_{3}\) with respect to the visual nodes \(V_{1}\) and \(V_{2}\). _Area coverage_ applications, in turn, monitor a region and analyzes the information from this entire area, aiming to identify some phenomena in any part of the image. In the case of the WVSN presented in Figure 3, the area is represented by the marked region. Examples of such applications are environmental monitoring for fire alert, traffic monitoring, intrusion detection, etc. Finally, _barrier coverage_ is used to monitor a specific sub-region with a shape of a continuous path or belt, built by the intersection of some visual nodes, expecting to identify any phenomena that happen crossing this barrier. For instance, in Figure 4 any crossing path from the border **A** to the border **B** (or vice versa) is intercept in some point by the barrier belt marked. This type of coverage canF be applied for intrusion detection, border surveillance and other defense application, such as in military zones and battlefields. For these cases of spatial coverage, we propose to address quality of WVSN by some quantitative perspectives, which is the percentage of coverage, the redundant coverage and the minimum sensors set. Therefore, in this section we discuss surveyed articles that assess the performed coverage associating the coverage quality to the amount of covered area, to the quantity of viewed targets or to the extension of breadth of the barrier. These aspects can be co-related to others performance metrics, such as lifetime, energy consumption and occlusion.