## 5 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Configuration of the Sources** & **SR\({}_{(1)}\)** & **SR\({}_{(2)}\)** & **SR\({}_{(3)}\)** & **SR\({}_{(4)}\)** & **SR\({}_{(5)}\)** \\ \hline SR-based(1) & 15 & 12 & - & - & - \\ SR-based(2) & 15 & 3 & - & - & - \\ SR-DWT-based(2) & 15 & 3 & - & - & - \\ SR-based(3) & 7 & 15 & 11 & 15 & 8 \\ SR-based(4) & 2 & 6 & 3 & 1 & 15 \\ SR-DWT-based(4) & 2 & 6 & 3 & 1 & 15 \\ SR-based(5) & 6 & 9 & 15 & 8 & 7 \\ \hline \end{tabular}
\end{table} 
Table 5: Sensing relevancies configurations. Figure 6: Communication scenarios for the simulations. The energy consumption in node n1 after 12 days of continuous transmission in Scenario 1 is presented in Figure 7, for different configurations of the three energy thresholds. As expected, the worse results in terms of energy consumption are achieved when traditional SR-unaware relaying mechanism is employed. Note that the same energy is consumed whatever the values of \(e_{I}\), \(e_{2}\) and \(e_{3}\). In a different way, the SR-based approach performs differently depending on \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Energy Level**} & \multicolumn{2}{c}{**Packets that MUST be relayed to the next hop**} \\ \cline{2-3}  & **DR** & **DWT Subband** \\ \hline \(e\geq e_{I}\) & 0, 1, 2 and 3 & HH, HL, LH and LL \\ \(e_{2}\leq e<e_{I}\) & 1, 2 and 3 & HL, LH and LL \\ \(e_{3}\leq e<e_{2}\) & 2 and 3 & LH and LL \\ \(e<e_{3}\) & 3 & LL \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Packet forwarding for the Data-relevance relaying approach. the sensing relevancies of the source nodes (s1 and s2). The Data-relevance relaying approach has a good performance concerning energy consumption, being slightly better in Figure 7b. Note, however, that all snapshots are lower-bounded when the Data-relevance approach is employed, whatever the relevancies of the source nodes. In a different way, the SR-based approach will always delivery high-quality images for the highest relevant source nodes, potentially enhancing applications overall monitoring quality. A visual scheme of the expected quality of the received images at the sink side is represented in Figure 8. High-quality images will be always delivered for a traditional relaying approach, while images with decreasing quality will reach the sink for the Data-relevance approach according to the energy thresholds and the current energy resources of intermediate nodes. For the SR-based approach, only images from the maximum-relevant source node (SR\({}_{(1)}=15\)) will always reach the sink without loss of quality. The monitoring quality when employing the proposed SR-based relaying approach will decrease along the time, according to the energy level and pre-defined thresholds. In initial states of the network, images from all source nodes will be transmitted to the receiving end, but the amount of visual information that will reach the sink will decrease along the time. However, images from maximum relevance source nodes will be always relayed without interference, preserving data with highest significance for the application. The energy consumption of node n2 for transmissions over Scenario 2 is presented in Figure 9 for the source nodes configurations SR-based(3) and SR-based(4). Once more, the energy consumption of the proposed SR-based relaying approach is highly related to the sensing relevancies of source nodes. When all source nodes have maximum relevance, the performance is similar to a traditional relaying paradigm. However, energy savings are achieved when low-relevant sources nodes are identified. Visual data transmitted from those source nodes are indeed important, but applications can afford some data losses if visual data from high-relevant source nodes are received for longer time. Figure 8: Average quality of the images that reach the sink for different transmission configurations. Figure 10: Relation between energy consumption and the defined energy thresholds. Figure 9: Energy consumption for Scenario 2.