# A Cross-Layer Optimization QoS Scheme in Wireless Multimedia Sensor Networks
## 3 Problem Formation and Dynamic Algorithm
### Comparison of Performance
The average throughput of real-time sessions of different schemes is compared in Figure 5. The average throughput of real-time sessions under CLADS-BCD, CLADS-MAC, and EASYO all increased with the increase in the number of nodes. In Figure 5, we can see that average throughput of CLADS-BCD was lower than that of EASYO. ACH performed much worse than backpressure-style schemes. The average throughput of non-real-time sessions of different schemes is compared in Figure 6. In Figure 6 we can see that the average throughput of non-real-time sessions under CLADS-BCD, CLADS-MAC, and EASYO all increased with the increase in the number of nodes. Figure 6 also shows that the average throughput of CLADS-BCD was lower than that of EASYO, but higher than that of CLADS-MAC and ACH. The reason is as follows. Both the data transmission time and throughput under ACH were reduced, since TDMA scheduling was utilized in clusters. Since the  
Figure 4: Comparison of the ratio of survival nodes out of region of radius R.  
Figure 3: Comparison of the ratio of survival nodes in the region of radius R.  
weights of data queues in the optimization framework in CLADS-BCD were lower than those in the EASYO scheme, CLADS-BCD and CLADS-MAC performed worse than EASYO in average throughput. It should be noted that the average throughput of real-time sessions under CLADS-MAC was the highest. The reason is that in CLADS-MAC, using a distributed scheduling scheme, the nodes with more buffering packets could obtain more transmission opportunities. This means that CLADS-MAC is suitable for wireless multimedia ad hoc networks in which nodes are initialed with adequate energy. The average end-to-end delay of real-time and non-real-time sessions of different schemes is compared in Figure 7 and Figure 8, respectively. As Figure 7 shows, CLADS-MAC performed best in the average end-to-end delay of real-time sessions. The reason is that in the scheduling of CLADS-MAC, the nodes with more buffering packets may obtain transmission opportunities with higher probability, and this can reduce average end-to-end delay. According to the results in Figure 5 and Figure 7, CLADS-MAC is the most effective scheme for real-time services in wireless ad hoc networks in which nodes have enough energy. Though the average end-to-end delay of real-time sessions under CLADS-BCD was the highest, it was lower than the end-to-end delay deadlines of real-time sessions, which was \(1\) s. In Figure 8, the average end-to-end delay of non-real-time sessions under CLADS-BCD was still the highest, and was lower than the end-to-end delay deadline of non-real-time sessions, which was set to be \(4\) s. This implies that the differences between the average end-to-end delays of various schemes will not lead to differences in the service qualities of applications. The reason is as follows. The weights of the data queues in the Lyapunov optimization framework, as well as the data transmission priorities under CLADS-BCD, were lower than those under EASYO. Figure 5: Average throughput of real-time sessions under different number of nodes. Figure 6: Average throughput of non-real-time sessions under different number of nodes. Therefore, the average end-to-end delay under CLADS-BCD is higher. However, a DQS scheme is utilized to maintain data queues in CLADS-BCD. In each data queue, a transmission opportunity is distributed to the packet with the lowest survival time. Therefore, CLADS-BCD can provide QoS guarantees on the average end-to-end delay. CLADS and EASYO are both backpressure-based cross-layer algorithms. In Figure 9, Figure 10, and Figure 11, the performances of CLADS-BCD and EASYO are compared. To limit the duration of this simulation, the initial energy of non-real-time nodes and multimedia nodes was set to be 0.01 J and 0.04 J, respectively. In Figure 9, network lifetime is compared. As shown in the figure, network lifetime under CLADS-BCD was much higher than that under EASYO. The reason is as follows.