# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 3 Reviewing Spatial Coverage Quality
### Target Coverage  
The paramount quality metric in target coverage applications is the achieved number of covered targets, which defines the coverage rate, i.e., the percentage of the covered targets among all the targets [31; 32; 33; 34; 35]. Target coverage is commonly stated in terms of \(k\)-coverage, requiring that each target must be covered by at least \(k\) visual nodes [36; 37]. This is an useful quality metric, since it expresses the redundancy coverage level of the targets [38]. However, if these metrics (coverage rate and \(k\)-coverage) are not associated with other metrics, they are little informative regarding to the ratio between available cameras and existing targets. For instance, a WVSN can be 3-coverage, with 5 targets and 15 visual, or with 5 targets and 30 visual nodes. In this last case, there is obviously a bad deployment issue. Additionally, \(k\)-coverage by itself does not qualify the effort to perform the coverage. In fact, different networks implementations can cover the same set of targets with different power consumption [32; 33; 34].  
Figure 4: WVSN for barrier coverage.  
Figure 3: WVSN for area coverage.  
Other way to address target coverage quality of an application is to define a required coverage quality value for each target. The required value must be achieved by the cumulative quality performed by all sensors in relation to that target [39; 40; 41; 42; 43; 44; 45; 46; 47]. This can be seen as a variation of \(k\)-coverage. Instead of at least \(k\) nodes cover a target, as many nodes as the necessary must cover a target in order to, together, achieve the required quality. In other words, the target effective quality is the sum of the coverage quality of each visual sensor covering a specific target. This sum must be greater or equal to the required quality of that target. If this happens for all targets, the application fulfill its quality requirement. The required quality of a target can be the sharpness of with which the target is seen by the sensor nodes [39; 40; 43], the probability of a target to be close enough of a camera to be satisfactory seen [44; 45], or the priority of monitoring [41; 46; 47] (a measure of the perceived importance).  
A required coverage quality may be reached in association with other metrics, such as lifetime. For this case, lifetime can be defined as the time duration when all targets have their quality requirements fulfilled. Thus, visual nodes can be deployed choosing orientations that cover all targets, fulfilling quality requirement, spending the minimum energy possible [39]. To achieve a lower power consumption, visual nodes with inefficient or redundant coverage can be deactivated or their sensing range can be adjusted. However, notice that redundancy is an implicit issue on \(k\)-coverage and required coverage quality approaches, since it is generally necessary more than one visual node covering a single target.  
To cope with the trade-off between the number of active sensor nodes to achieve the quality requirements and the energy consumed, optimization methods can be applied in order to redeploy the WVSN, minimizing power consumption and maximizing the network lifetime, such as greedy algorithms [39; 40; 42; 43], genetic algorithms [41; 46], learning automata-based scheduling algorithms [39], integer linear programming [42; 44; 45], integer nonlinear programming [47], integer quadratic programming [42].  
Besides power consumption, other aspects can also affect the network lifetime and, as consequence, they can affect the quality coverage, such as network connectivity and energy harvesting [47]. Network connectivity is related to the ability of the active nodes be connected and deliver information to the base station, while energy harvesting (solar energy, thermal energy, wind energy) is related to the ability to compensate power consumption with additional power supply.