# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 5 Performance Evaluations
### Multi-Traffic Scenario  
Hereafter, we consider the heterogeneous network model to emphasize the effect of the proposed multi-queue model in CCTD. Figure 15a,b shows the worst-case E2E comparison of T\({}_{1}\) and T\({}_{2}\) traffic, respectively, under different traffic rates of T\({}_{3}\). The E2E delay was calculated as the time elapsed from the generation of the packet until it was successfully delivered to the DODAG root. As shown in the two figures, the proposed CCTD scheme significantly reduced the worst-case delay of critical traffic T\({}_{1}\) and T\({}_{2}\) compared to RPL-OF0 and CoAR. With the proposed priority-based multi-queue model, critical packets were always given higher priority for transmission as long as Q\({}_{1}\) and Q\({}_{2}\) were non-empty where packets from Q\({}_{1}\) were given the highest priority. Such a function was not provided either by RPL-OF0 or CoAR, where packet transmissions followed an FIFO-based single-queue model regardless of the priority and the criticality of the traffic. Hence, critical data were likely to be blocked by the transmission of multiple non-critical data as the latter arrived first. The situation became even worse at high traffic rates of T\({}_{3}\) as more traffic was queued ahead to the critical data, which further degraded its delay performance, while CCTD preserved a steady lower delay regardless of the T\({}_{3}\) traffic load as shown in Figure 15a,b. Moreover, buffer overflow was likely to occur in RPL-OF0 and CoAR with the single queue model in heavy traffic scenarios where T\({}_{1}\) and T\({}_{2}\) packets were dropped and needed to be retransmitted, which added significantly to the delay. Quantitatively, compared to CoAR, CCTD reduced the worst-case delay of T\({}_{1}\) by at least 78% while it achieved a 68% reduction for T\({}_{2}\). The introduced service differentiation was further illustrated through the Cumulative Distribution Function (CDF) comparison of E2E delay as shown by Figure 16a,b where we plot the CDF at 120 ppm/node in CCTD and CoAR, respectively. Figure 16a clearly shows the service differentiation between the different traffic types in CCTD as a result of the proposed multi-queue model where T\({}_{1}\) packets in Q\({}_{1}\) were given the highest priority priority, while T\({}_{3}\) packets were given the lowest priority. However, Figure 16b shows that all traffic in CoAR almost followed the same trend for the E2E delay distribution. In CoAR, the packet in the output buffer followed the FIFO scheduling policy; hence, on average, all packets experienced identical E2E delay. Figure 14: PDR performance comparison for different network sizes with a traffic rate of 90 ppm/node. The results in Figure 15(a) also reflected on-time PDR of both T\({}_{1}\) and T\({}_{2}\). The on-time PDR denotes the percentage of packets delivered within a predefined deadline. As mentioned earlier, for critical traffic, out of the total packets delivered successfully to the DODAG root, those delivered within the specified deadline limit were meaningful to the system. For instance, CCTD achieved a one-time PDR of 86% considering a deadline of 400 ms for T\({}_{1}\), while the percentage was 92% considering a deadline of 500 ms for T\({}_{2}\). The introduced multi-queue model in CCTD had a direct effect on the delivery performance of T\({}_{3}\), as the buffered T\({}_{3}\) packets in Q\({}_{3}\) were suspended from transmission as long as Q\({}_{1}\) and/or Q\({}_{2}\) were non-empty. Hence, CCTD sacrificed the reduced PDR performance of T\({}_{3}\) in favor of the improved performance for T\({}_{1}\) and T\({}_{2}\). Figure 17 shows the PDR comparison of T\({}_{3}\) for different traffic rates. CCTD had a reduction of 32% in the PDR of T\({}_{3}\) at 180 ppm/node compared to CoAR, which was relaxed to 13% at 90 ppm/node. However, such degradation could be considered insignificant in critical IIoT applications where guaranteeing real-time performance of critical data is a primary priority [17].