## 6 Evaluation
### Experimental Setup  
Conventional simulators and their radio models fail to accurately model physical characteristics such as the capture effect or the distribution of the noise since they are too simplistic and usually overlook some details. For radios with narrow band modulation, it is not only the receiver threshold and non-ideal antennas that cause the transitional region. Multi-path fading plays a major role [34] in the connectivity of these radios, and transmission power control is a decisive parameter in managing the shadowing effect. Most simulators for low-power radios fall short of modeling the shadowing effect by considering a log-normal shadowing model. Real-world experiments are undeniably more accurate in this matter. Figure 5: Simulation topology for scenarios F and C. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Scenario** & **Topology** & **Clients/Servers** &
\begin{tabular}{c} **Traffic Load** \\ **(pk/min/node)** \\ \end{tabular} & **Explanation** \\ \hline A & Random & 10/1 & 6 & Upward traffic + sparse \\ B & Random & 40/1 & 6 & Upward traffic + dense \\ C & Three-cluster & 30/3 & 6 & (Up + Down)–ward traffic \\ D & Random mid-density & 20/1 & 60 & Upward traffic + congestion \\ E & Grid \(5\times 5\) unreliable links & 25/1 & 6 & RX Ratio \(-0.7\), Radio \(=\) LLM \\ F & Grid \(5\times 5\) mobile node & 25/1 & 6 & Mobile nodes \(=1\) \\ G & Random & 100/1 & 30 & In a \(200\times 200\) an area \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation scenarios and parameters. In order to obtain more accurate evaluations, we assembled a physical testbed containing 40 NRF5340 boards. The experiments were conducted using the modern NRF5340 development boards introduced by Nordic Semiconductor [33] featuring two Arm Cortex-M33 processors. The first processor, referred to as the network core, comes with 256 kB of flash and 64 kB of RAM, and it runs at a low power mode, while the application core provides a considerably higher computation capacity. To optimize the power consumption, this application core gives up control to the network core upon initialization and remains inactive thereafter. According to the datasheets, the NRF5340 affords the flexibility to configure 24 distinct settings for transmission power, offering a finer granularity of control over the transmission power in comparison to the eight levels available in sky motes [41]. In an effort to maintain a fair utilization of system memory, we opted to restrict the NRF5340 to employ eight transmission power settings. As shown in Table 2, NRF5340 consumes less energy compared to sky motes, owing to its more recent radio. Part of the experimental setup is illustrated in Figure 6. Ten Raspberry Pis (only two are shown) are powering up and controlling the NRF5340 boards. The Raspberry Pis are then managed through a _Secure Shell_ (SSH) protocol over a Wi-Fi access point configured to operate on a specific channel so that it does not interfere with the operation of NRF boards. The testbed was located in the corridors and rooms of the IDT school of the Malardalen University, as illustrated in Figure 7a. The experiments were performed using two settings with (i) orange nodes establishing a sparse topology for a 12-node scenario and (ii) utilizing all 40 nodes for a dense deployment. The running topology (for both ACTOR and default RPL) is dynamic, and it depends on time and the randomness caused due to the trickle algorithm that triggers the dissemination of DIO packets. The most recurring topology consists of almost half of the nodes being connected in a single hop, while the rest are being connected with two hop links (as demonstrated in Figure 7b. ACTOR-D and standard RPL ended up having very a similar topology but with a much lower rate of parent switching. Analyzing the normal Wi-Fi traffic in the office environment showed that channel 24 in IEEE 802.15.4 was the least disrupted. We fixed the communication of our testbed on this channel to minimize Wi-Fi interference.