## 3 Fundamental Concepts
### Sensing Relevance  
For many applications, quality of the deployed visual sensor network will be a function of how well an area of interest is viewed by source nodes. And such quality depends on the actual application requirements, which dictate what, when and with which constraints a set of static or moving targets must be monitored by the deployed camera-enabled sensors. This is an innovative concept aimed at the quality of viewing of the visual sensor network, directly related with the concept of Quality of Experience (QoE) [35]. In fact, many works in the literature have been concerned with sensing coverage, connectivity and energy preservation [5,22], but such issues have been treated in a generic way, with small or absent concern on the application requirements in terms of the actual targets that need to be monitored by deployed sensors. In many cases, source nodes may have different relevancies for a particular application, and such notion of relevance is more evident when source nodes follow a directional sensing model as in visual sensor networks. In fact, neighbor nodes in scalar wireless sensor networks tend to collect similar information, but that is not necessarily true for neighbor camera-enabled source nodes [5]. This particularity makes the sensing relevance an inner characteristic of visual sensors that is directed related with the applications monitoring requirements, whatever are their current positions. Thus, at a given time, source nodes with different relevancies for the application may be transmitting packets with encoded visual information, and such relevancies may be exploited to optimize the network operation [13]. Among the optimization strategies, the sensing relevancies may be used to prioritize data packets, granting higher relevance to packets transmitted from more relevant visual sensors. As energy is a major optimization issue, low relevant packets may be discarded to save energy, enhancing the performance of the network but still assuring that high relevant packets will (probably) reach the sink. We presented a broad discussion of sensing relevance in visual sensor networks and practical exploitation of such concept in [13]. The sensing relevance of each source node \(s\) is defined as a 4-bit numeric value, referred as the Sensing Relevance index (SR\({}_{\text{(s)}}\)) [13]. The sensing relevancies and, more precisely, the values of SR\({}_{\text{(s)}}\) are assigned to each source node according to two groups of information: the significance of the retrieved visual data for the application and the available monitoring resources in the considered source node. The significance of the retrieved visual data is used to classify each source node in a Group of Relevance (GR), where the final value of SR\({}_{\text{(s)}}\) is computed using GR and information about the available monitoring resources. The groups of relevance are described in Table 1.  
Considering the assigned GR, each source node \(s\) computes a final value for SR\({}_{\text{(s)}}\). For such computation, some parameters as residual energy, visual resolution and zooming capabilities can be accounted, computing a final value for SR\({}_{\text{(s)}}\) between 0 and 15. As presumed, the sensing relevance is a subjective concept that cannot be automatically computed only based on the network topology, requiring identification of the groups of relevance for each source node. Automatic and manual approaches to identify groups of relevance are described in [13], but we summarized the main computing approaches in Table 2.  
The sensing relevance is a global QoS parameter. Source nodes are classified in groups of relevance, which are indeed computed by the sink (or some element at the sink side), which has a global view of the network. In other works, the SR of each camera-enabled sensor has significance for