## 3 Fundamental Concepts
### Sensing Relevance
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group of Relevance** & **SR\({}_{\text{(g)}}\)** & **Description** \\ \hline \multirow{4}{*}{Irrelevant} & \multirow{4}{*}{0} & When the source node has no relevance for the application. \\  & & Source sensors should turn off their camera’s hardware, \\  & & acting only as relay nodes. \\ \multirow{2}{*}{Low relevance} & \multirow{2}{*}{1–4} & Source sensors are transmitting complementary visual \\  & & information with low impact to the application monitoring quality. \\ \multirow{4}{*}{Medium relevance} & \multirow{2}{*}{5–10} & This is the initial relevance group, when source nodes are turned on. \\  & & The transmitted information is relevant, but some quality \\  & & loss can be accepted. \\ \multirow{4}{*}{High relevance} & \multirow{2}{*}{11–14} & Some sensors will have higher relevance for the application, \\  & & requiring prioritized treating in packet processing, congestion \\ \multirow{4}{*}{Maximum relevance} & \multirow{2}{*}{15} & control, error recovery and multipath selection algorithms. \\  & & This is the highest level of relevance that should be attributed \\ \multirow{4}{*}{Maximum relevance} & \multirow{2}{*}{15} & to a very small group of source sensors, if any. Monitoring quality is \\  & & highly dependent on visual data transmitted by these source nodes. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Groups of relevance and the associated Sensing Relevance index (SR). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Advantages** & **Drawbacks** \\ \hline \multirow{4}{*}{Deterministic establishment of the groups of relevance. Source nodes are assigned to a GR before deployment.} & \multirow{4}{*}{No computational costs. Source nodes are statically assigned to a group of relevance.} & Useful only for deterministic deployment. \\  & & & May not adapt to changes \\  & & of relevance. & in the network topology \\  & & & and in the targets positions. \\ \multirow{4}{*}{Visual identification of the groups of relevance by a human operator.} & \multirow{4}{*}{Sensing relevancies of source nodes strongly reflect the application requirements.} & \multirow{4}{*}{Regular to interpret the visual information retrieved from the monitored field.} \\  & & Minimal computational costs. & Subject to unconscious \\ \multirow{4}{*}{Computer vision algorithms are employed to identify the targets viewed by source nodes. The GR is indirectly established according to the relevance of the targets.} & \multirow{4}{*}{Automatic establishment of sensing relevancies very close to the application requirements.} & High computational cost. \\  & & & depends on the nature of the targets and the monitored field. \\ \multirow{4}{*}{Medium-localization algorithms are used to identify areas of interest. The GR is established if source nodes view the defined area.} & \multirow{4}{*}{Groups of relevance may be established for regions.} & \multirow{4}{*}{Depends on camera-enabled node localization algorithms.} \\ \cline{1-1} \cline{4-4}  & & & More suitable for \\ \cline{1-1} \cline{4-4}  & & & scene/area monitoring [5]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some approaches for computation of the groups of relevance. the entire network, since it is computed based on the monitoring requirements of applications. Moreover, it is easier to compute and implement, requiring only an specialized application-layer protocol to allow the proper communication between source nodes and the sink, as the one proposed in [13]. Figure 1 presents a practical example of how the sensing relevancies of source nodes may be established according to application monitoring requirements. Two representations of the exactly same forest are showed, where the figures of animals indicate the places where they are more likely to appear. The field of view of source nodes is represented by a pyramidal area. In each of the presented monitoring scenarios, a VSN is deployed for monitoring of blue macaws.