## 6 Evaluation
### Simulation Results  
Figure 8 summarizes the results of the conducted simulations in terms of PDR, the end-to-end delay, and the current used for transmission. Figure 8b plots the average PDR and shows that ACTOR and ACTOR-D achieve a lower packet loss compared to RPL and the benchmarks in dense (B), multi-cluster (C), high-rate (D), lossy (E), mobile (F), and 100-node (G) scenarios. This is mainly due to spatial reuse in ACTOR that overcomes the congestion in the wireless medium. In a dense network, ACTOR exhibits better resilience against heavy traffic loads compared to TPP and XRPL. In scenario A, ACTOR leads to negligible loss (attributable to exploration), indicating that reducing the transmission power in a sparse network is not as beneficial as in a dense network. Under ACTOR, CSMA nodes happen to have fewer backoffs and failed CCAs. This leads to a significant improvement in the delay for ACTOR in scenarios C, D, E, and G which is demonstrated in Figure 8a. Like PDR, ACTOR outperforms RPL, TPP, and XRPL in terms of end-to-end delay in the dense scenarios, confirming that ACTOR reduces channel contention. In order to test the scalability of the system, we included a scenario with 100 randomly positioned nodes. As you can see, in scenario G, ACTOR improves the delay and current consumption (Figure 8c) while providing the same level of PDR. Please note that, in scenarios E and G, RPL provides a very small delay, which is irrelevant since most of the packets are dropped by RPL. In ACTOR, all the nodes  
Figure 7: The NRF5340 nodes are placed in the corridors of the Engineering School at Malardalen University (**a**). The 12-node experiment only used the orange nodes, while in the 40-node experiment, all the nodes were used. The most recurring topology that the network converges to is a tree with a maximum distance of two hops (**b**). send a packet to their parents to indicate the minimum required transmission power to agree on a transmission power that does not jeopardize the routing protocol. When normal data transmission is challenged due to intensive collisions, the overhead caused by the negotiation can be excessive. Specifically, in scenario G, ACTOR nodes transmitted 12 thousand extra packets for transmission power negotiations with all 100 of the nodes during the 10 min, while the transmitted data packets summed up to almost 30 thousands. Despite this overhead, the optimal selection of transmission power of ACTOR led to good results in such large scale. We also measured the current consumption using Energest ([[https://docs.contiki-ng.org/en/develop/doc/programming/Energest.html](https://docs.contiki-ng.org/en/develop/doc/programming/Energest.html)]([https://docs.contiki-ng.org/en/develop/doc/programming/Energest.html](https://docs.contiki-ng.org/en/develop/doc/programming/Energest.html)) accessed on 3 January 2024), which is a software-based energy estimation module. Figure 8c depicts the average current consumption for all of the nodes. ACTOR consistently (even in a sparse scenario) achieves lower energy consumption due to its regret-optimal transmission power selection. Reducing the transmission power not only reduces the per-transmission energy but also leads to fewer retransmission of packets, further improving the energy consumption. ACTOR nodes report (i) less time spent with their radio in transmission mode and (ii) a lower transmission power. In the dense and multi-cluster topology, the benefits of employing ACTOR are even more palpable. In the sparse topology (A), the nodes cannot meaningfully reduce their transmission power. Hence, ACTOR cannot demonstrate its advantages in PDR, and there is only a small gain in terms of energy consumption. Nevertheless, ACTOR may sacrifice a few data packets to explore lower transmission power settings that are not helpful in a sparse setting, and this leads to lower PDR. ACTOR tries to minimize this so-called _exploration loss_ to a negligible level via its blacklisting mechanism and the UCB exploration strategy.