## 4 Results
### Dynamic Traffic Flows  
We now analyze the performance of the proposed solution in a more dynamic setup, where traffic sources are changed during the experiment. We carry out this analysis only over the \(TP_{3}\) scenario, using nine traffic destinations. The system configuration is similar to the one described in the previous section, except for the fact that traffic sources are changed every 25 time slots. This way, the accumulated exposure of a node, being part of one route during the whole experiment, would be no greater than \(\epsilon\cdot T\).  
Over this setup we study the relationship between \(\mathcal{K}\) and \(\mathcal{P}\) in two different ways. First, we vary the growing pace of \(\mathcal{K}\), so that its relative importance compared to \(\mathcal{P}\) also varies. In addition, we apply an aging function to \(\mathcal{K}\), so that the effect of the accumulated exposure vanishes when a node is not traversed by any traffic flow. Table 2 shows the configurations we used. \(\mathcal{P}\) is computed as described earlier (proportional to the distance), while the accumulated cost of a node is increased by \(\epsilon\), each time unit, for each flow traversing the node. For convenience, we use \(\Delta K\) instead of \(\epsilon\). Then if a node is not used by any route, its accumulated exposure cost is reduced by \(\
abla K\) units, each time slot. Altogether, in each slot \(t\) every node updates the accumulated exposure metric, \(\mathcal{K}(t)\), as shown in Equation (8).  
\[\mathcal{K}(t)=\begin{cases}\mathcal{K}(t-1)+n\cdot\Delta K&\text{if there were $n>0$ active routes within the interval $[t-1,t]$}\\ \min\left\{0,\mathcal{K}(t-1)-\
abla K\right\}&\text{if there was not any active route within the interval $[t-1,t]$}\end{cases} \tag{8}\]  
Figure 10 depicts the average value of the accumulated exposure obtained from 100 independent runs. It is represented using the generic quantity \(\epsilon\), to allow a fair comparison between the different configurations. In general we can see a negative impact of changing the traffic sources. In this sense, the exposure gain compared with the _MinPower_ solution in Figure 10, when \(\
abla K\) equals 0, is lower than the one observed in Figure 8. On the other hand, we can see that the use of aging functions has a positive effect. In short, when they are not used, nodes that were previously selected are unlikely to be part of a new route until other nodes reach similar accumulated exposure, which might lead to longer routes and therefore higher overall exposure. On the other hand, when we included the aging function, nodes might get re-selected sooner, lowering the overall system exposure. In addition, the results also evince that the aging function is only noticeable when \(\Delta K\) is equal or greater that 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cost Parameter** & **Value** \\ \hline \(P\) & distance (m) \\ \(\Delta K(\epsilon)\) & \(\{3,6,9,12,15\}\) \\ \(\
abla K\) & \(\{0,...,\Delta K\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Costs configuration.  
Figure 9: Mean power obtained per generated route. We represent the values yielded by the _EMFarware_ and _MinPower_ algorithms with markers and bars, respectively.