# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 5 Performance Evaluations
### Single-Traffic Scenario
The QLR was calculated as the total packets lost due to queue overflow divided by the total generated packets in the network. Although queue losses and buffer overflow are inevitable under heavy load conditions, efficient load-balancing and congestion control could mitigate such problems. The proposed CCTD approach managed to reduce the QLR of RPL-OF0 by 79% at 150 ppm/node as a result of the adopted congestion control framework, which helped achieve a fair load distribution among intermediate nodes. On the other hand, the proposed probabilistic parent selection strategy in Equation (10) and the improved Trickle timer algorithm together helped to improve the QLR performance compared to that achieved by CoAR. For instance, CCTD improved the QLR of CoAR by 45% at 150 ppm/node, which was increased to 53% at 180 ppm/node. As mentioned earlier, RPL was implemented on LLN resource-constrained nodes with small queue sizes, and these queues started to overflow under heavy traffic load. A solution could be to increase the buffer size of intermediate nodes to alleviate the congestion problem. However, such a solution was ineffective in the case of imbalanced RPL networks without a proper parent selection mechanism. To further illustrate, Figure 11 shows the QLR performance of RPL-OF0 after increasing the Buffer Size, denoted as BS in the figure, to 20 and 40. For the sake of comparison, we also added the QLR of CCTD with the BS of 10 packets. As depicted by Figure 11, increasing the buffer size to the double value marginally improved the QLR in RPL-OF0 under heavy traffic conditions. For instance, the QLR was reduced by 9% when increasing the BS from 20 to 40 at 120 ppm/node, while it was reduced to only 3% at a traffic rate of 180 ppm/node. On the other hand, CCTD with a BS of 10 maintained improved QLR performance over RPL-OF0 under heavy traffic rates. In order to further prove the effectiveness of the proposed method, Figure 11 shows that at a rate of 150 ppm/node, RPL-OF0 could achieve almost the same QLR as CCTD when increasing the BS to 220. Such a queue size is, however, infeasible in practice given the current resource-constrained LLN devices. As the QLR reduced, the proposed CCTD in turn enhanced the PDR performance compared to RPL-OF0 and CoAR, as shown in Figure 12. The effectiveness of the proposed CCTD was clearly observed at heavy load conditions where CCTD improved the performance of CoAR by 33% and 59% at 150 ppm/node and 180 ppm/node, respectively. Figure 13 shows the hop-count comparison between the three methods against different traffic rates. The figure includes both the average and the maximum hop-count to the DODAG root. As can be noted from the figure, the proposed CCTD had a marginal effect on the hop-count of RPL-OF0 as nodes may select a path with a higher hop distance towards the DODAG root to alleviate the congestion effect. Moreover, CCTD exploited the hop-count metric in \(R^{LB}(p_{i})\) as shown by (7) when selecting the alternative parent; therefore, CCTD incurred a slight increase in this metric. Furthermore, the parameter \(\lambda\) could be further tuned to achieve a trade-off between congestion control and hop-count. However,  
Figure 10: Queue Loss Ratio (QLR) comparison for different traffic rates. Hence, congestion control and load balancing become more important and an effective way to improve the performance of RPL in large-scale networks. Accordingly, as shown in Figure 14, CCTD maintained its PDR performance enhancement and even achieved more improvements as the network size increases. Specifically, CCTD improved the PDR by 64% compared to RPL-OF0 with 30 nodes, while the percentage was dramatically increased to 275% with 150 nodes. In this context, it is reported that in process automation scenarios, the network would include a maximum of 50 nodes in order to meet the required refresh rates [43].