## 5 Performance Evaluation
### Simulation Results  
We first evaluate the impacts of various traffic loads and bit error rates (BER) on the average end-to-end packet delay and the on-time packet delivery ratio. While the first parameter is measured as the average delay experienced by all classes of delivered packets to the sinks, the latter one is the ratio of the total number of packets received by the sinks to the number of packets generated by the sources. Impact of Traffic LoadsThe modular and distributed architecture of the proposed DMQoS framework helps it to achieve significant performance improvements over the state-of-the-art QoS-aware routing protocols, as shown in Figure 3. Recall (from Algorithms 1 and 2) that DMQoS optimizes the number of packet duplications based on measured link reliability (\(r_{i,j}\)) values and chooses the next-hop router that has the highest velocity toward the destination sink. The velocity prediction of DMQoS is more accurate than those of other methods since it takes into account both transmission and queuing delays at neighbor nodes. Furthermore, the routings of different classes of data packets are handled distinctly, giving highest priority to the critical traffic flows, thus ensuring more reliable and faster delivery of important information to the destination. As source traffic load increases, the media contention increases as well, which in turn raises packet delays at each hop toward the destination. Figure 3(a) shows that, in all approaches, the end-to-end packet delay rises to higher values with increasing traffic load. The DMQoS offers the lowest end-to-end delay, and the gaps amongst the graphs are widened as traffic volume increases, indicating that DMQoS is more capable of handling high traffic volume compared to other approaches. On the other hand, MMSPEED suffers from the highest delay followed by LOCALMOR and DARA. Through careful examination of the details of our simulation, we observe that mainly exponential increase in packet duplication in MMSPEED, blind duplication of data packets toward primary and secondary sinks in LOCALMOR and the use of the same aggregate routing function both for critical and non-critical traffic in DARA cause more contention in the wireless medium, resulting in increased end-to-end packet delivery delays. Figure 3(b) shows that DMQoS demonstrates superior performance in terms of on-time packet delivery ratio, indicating that packet drops can greatly be reduced when source data packets are dispersed toward multiple sinks in a way that implements load-balancing among the paths by judiciously choosing \begin{table}
\begin{tabular}{|l|c|c|c|c|c|} \hline Packet ClassSource sets & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(S_{4}\) & \(S_{5}\) \\ \hline CP & 5 & 10 & 20 & 30 & 40 \\ \hline DP & 10 & 20 & 40 & 60 & 80 \\ \hline RP & 20 & 40 & 60 & 80 & 100 \\ \hline OP & 30 & 60 & 80 & 100 & 120 \\ \hline \end{tabular}
\end{table} 
Table 3: Five different sets of source traffic loads. the more reliable links and lightly loaded next-hop router nodes along the path. The simulation results indicate that the poor performances of LOCALMOR and MMSPEED protocols are primarily due to the excessive media contentions caused by huge numbers of duplicate packets in the networks. We also plotted the average end-to-end packet delays and delivery ratio experienced by CP traffic only in Figure 3(c) and Figure 3(d), respectively, and found that our DMQoS outperforms the existing approaches, evidencing the advantage of using the data-centric and scalable modular QoS-aware routing approach. Impact of Bit Error RateIn what follows, we investigate the impacts of wireless link bit error rates (BER) on the end-to-end packet delay and the reliability. We vary the BER value ranging from \(10^{-6}\) to \(10^{-2}\), keeping the data traffic load fixed at \(S_{3}\). With the increase of bit error rates packet delivery delay increases, and the probability of successful packet delivery decreases.