## 6 Experimental Evaluation of SRF-IoT Framework
### Evaluation Results  
Evaluation results from simulating the SRF-IoT scheme are presented in this subsection. From this point, we reference to malicious scenario using MRHOF as BHR scenario, normal scenario using MRHOF as normal scenario, and malicious scenario using SRF-OF as SRF-IoT scenario. Starting with Figure 4, the Median (PDR) of SRF-IoT framework is presented for scenarios where 5 to 15 SRF-IDS detectors are deployed. Overall, PDR in monitored network is kept at high levels with the help of the SRF-IoT framework. This can be clearly identified from the figure, as the median PDR starts from 90.8% when 5 detectors are deployed, then reaches the maximum of 92.8% in scenario with 7 detectors, and then the PDR declines as the number of detectors increases, reaching the minimum of 82.4% with 15 SRF-IDS detectors. This is expected because multiple SRF-IDS detectors generate extra overhead in the network and monitored nodes may receive other nodes' metrics, not related to their direct neighbours. Therefore, monitored nodes drop more unrelated packets, reducing the PDR metric. A comparison of the Median PDR of different scenarios is shown in Figure 5. Normal scenario has a median value of almost 95% in all simulated cases, whereas in the BHR scenario this percentage drops more than 15%. This big difference shows how attackers can degrade the performance of the network. Comparing the median number of PDR in SRF-IoT scenarios versus normal scenarios, we can see that a small difference of a minimum 5% and maximum 13% exist. This means that as more SRF-IDS detectors are deployed, the SRF-IoT framework's performance declines. However, those results indicate that SRF-IoT framework can assist nodes to avoid extra processing and energy overhead and operate in the same levels as in normal scenarios. Looking at the Median Parent Switch in Figure 6, results depict that our proposed framework requires fewer than 200 parent switches when deploying fewer than 15 SRF-IDS detectors. Specifically, the bar chart shows that SRF-IoT scheme has a median of 140 parent changes with 5 deployed SRF-IDS detectors, declining to 97 changes with 7 detectors, and then going up to 258 when SRF-IDS detectors are 15. It is clear that when having more than 12 SRF-IDS detectors, parent changes are almost doubled. An explanation is that as we increase SRF-IDS nodes, multiple detectors monitor similar nodes, and they send multiple metrics for the same monitored nodes. This could lead to high or low trust values which in turn leads to parent changes. The normal scenario indicates that parent switch should occur fewer than three times per node on average. Figure 4: Median packet delivery ratio (PDR) results in SRF-IoT scenario after deploying 5 to 15 SRF-IDS detectors. Figure 7 compares the values of the Median Parent Switch metric for three different scenarios: SRF-IoT, BHR, and normal. Generally speaking, the scatter chart indicates that SRF-IoT framework achieves low parent changes, with a small increase from the levels of normal scenarios. Looking more closely, using the SRF-OF, nodes change parents almost three times less than in BHR. Attacking the network causes approximately 600 parent changes on average, whereas in SRF-IoT we have fewer than 190 switches, and in normal scenarios we have a median of 30 switches. The only exception is the case with 15 SRF-IDS detectors in which SRF-IoT sees a surge in parent changes. The high parent switch number explains the low PDR that we previously saw in that scenario. Assuming the selected parent is an attacker, we expect to have more dropped packets, affecting the PDR metric. This means malicious actors successfully affect the network performance using rank and blackhole attacks. Another conclusion is that the SRF-IoT scheme drastically reduces parent switches in almost all cases, and even in the worst case it keeps the network more stable than in BHR scenario in which both PDR and parent switches metrics are high.