## 6 Evaluation
### Simulation Setup  
The simulations were conducted using the built-in simulator of Contiki, Cooja, which comes with plenty of options to model the radio models, including the _Unit Disk Graph Model_ (UDGM) and the _Logistic Loss Model_ (LLM). The UDGM model plots two disks around each node, representing the connectivity range and the interference area. LLM provides a more realistic model, as it uses the logistic function to calculate the reception probability of packets based on RSSI. The simulated boards are Sky Motes with TI's MSP430 F1611 controller with 10 kB of RAM and 48 kB of flash. They are equipped with a cc2420 radio that supports eight different settings for transmission power. This choice of sky motes was made to demonstrate the possibility of running the algorithm on legacy COTS since, in the experiments, we used a rather modern board. The current consumption associated with all the transmission power settings for sky motes is detailed in Table 1. For the NRF motes that are used in the experiments, the possible transmission powers are shown in Table 2.  
We considered 7 different scenarios, as detailed in Table 3. The parameters in these scenarios were chosen deliberately to exemplify challenging conditions for routing and transmission power control.  
* Scenario A represents a small-scale network with nodes positioned at distances where there is no benefit in reducing the transmission power. This scenario portrays a sparse deployment that is challenging for ACTOR. In such a deployment, ACTOR is expected to converge on high transmission power settings, and trying lower transmission power will only waste resources.
* In scenario B, the parameters were chosen to exhibit density. Forty nodes were positioned randomly in a \(100\times 100\) square. If the RPL nodes transmitted at their  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transmission Power (dBm)** & **Current (mA)** \\ \hline
0 & 17.4 \\ \(-1\) & 16.5 \\ \(-3\) & 15.2 \\ \(-5\) & 13.9 \\ \(-7\) & 12.5 \\ \(-10\) & 11.2 \\ \(-15\) & 9.9 \\ \(-25\) & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sky mote’s current consumption for different transmission power settings (used in simulations).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Transmission Power (dBm)** & **Current (mA)** \\ \hline
+3 & 5.1 \\
0 & 3.4 \\ \(-4\) & 2.7 \\ \(-8\) & 2.2 \\ \(-12\) & 2.0 \\ \(-16\) & 1.8 \\ \(-20\) & 1.7 \\ \(-40\) & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: NRF5340’s current consumption for different transmission power configurations (used in the experimental testbed).  
maximum power (0 dBm) with a 50 m range, then most of the nodes would have a high number of neighbors (at least 20).
* Scenario C (illustrated in Figure 5) resembles a multi-cluster topology, and it is based on the multi-instance feature of RPL. If the nodes in a cluster manage their transmission power properly, the clusters will not interfere with each other. The hop distance of the nodes is less than in the previous scenario. With fewer children, parent nodes have more freedom to tune their transmission power.
* In scenario D, a high load of traffic is applied to a medium-dense network.
* Scenario E presents the same \(5\times 5\) grid with a lower link quality, and it uses the more realistic LLM link model.
* Scenario F includes a \(5\times 5\) grid and one mobile node. The mobility pattern is simple, and it can be dealt with using only the transmission power control. This topology is illustrated in Figure 5.
* Scenario G tests the scalability of the system in a topology with 100 randomly placed nodes.