# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Asymmetric Topologies
#### 7.2.1 Scenario A1: ZigBee-Only, One-Hop Routes  
Consider again the topology of Figure 1 as in Scenario S1, but now assume that node 4 generates more traffic load than node 5, making the rate of interfering transmissions in node 1 greater than the rate of interfering transmissions in node 2, which results in \(c_{3,1,0}>c_{3,2,0}\). In this case, it is desirable that node 3 chooses the route 3,2,0 as much as possible to avoid the hidden node problem caused by node 4. We first evaluate the performance of the U-RR procedure in the single simulation run of the Example 2 of Section 5.1, in which nodes 3, 4, and generate an average of 20, 10, and 0.5 packets/s respectively. Figure 2f in Section 5.2 illustrates the cumulative costs measured at node 3 when using the U-RR procedure. Comparing this figure with Figure 2b, which refers to the same scenario but using the LS-based procedure, it is possible to observe that the U-RR procedure was able to generate estimates with less variation and better recognize that route 3,2,0 had lower cost than route 3,1,0 given the higher traffic caused by node 4 upon node 1. To better analyze the performance of the U-RR procedure at selecting the best route, we varied the traffic level generated by node 5, and at each traffic level, we repeated the simulation 30 times, each time with a different random seed. Figure 9a-f respectively, show the median and the 15 th to 85 th percentiles of \(\overline{c}_{3,1,0}\), \(\overline{c}_{3,2,0}\), \(\sigma_{3,1,0}\), \(\sigma_{3,2,0}\), \(r_{3,2,0}\), and node 3's number of retransmissions per 1000 messages generated for 30 simulation runs at each of node 5's traffic level for the LS-based, the LQI-based, and the U-RR procedures. Regarding the LQI-based procedure, as shown in Figure 9a,b, it estimated both routes to have cost 2 regardless of the traffic load generated by node 5. This means that node 3 chose the best route 3,2,0 randomly, around 50% of the times, as shown in Figure 9e. Regarding the LS-based procedure, it struggled to recognize route 3,2,0 as the best route. Although Figure 9a,b shows that the LS-based procedure resulted in \(\overline{c}_{3,1,0}>\overline{c}_{3,2,0}\), the difference was within the standard deviation of the cost estimates, as shown in Figure 9c,d. The higher variation of cost estimates caused the LS-based procedure to select route 3,2,0 only 60% of the time when node 5 generated only 0.5 packets/s, as shown in Figure 9e. In contrast, as illustrated in Figure 9a through Figure 9e, when node 5 generated 0.5 packets/s, the differences in cost estimates of the U-RR procedure were much higher than the standard deviation, and the U-RR procedure chose route 3,2,0 only 89% of the times. The U-RR procedure also resulted in a lower number of retransmissions: as shown in Figure 9f, the median number of node 3 retransmissions per 1000 messages was 115 when using the LQI-based procedure, 110 when using the LS-based procedure, and 83 when using the U-RR procedure. As the traffic load generated by node 5 increased, the true cost \(c_{3,2,0}\) started to increase above the cost 2; however, because of the cost rounding discussed before, the cost estimates by the U-RRprocedure started to alternate between cost 2 and cost 3, which is the cost of route 3,1,0, and \(r_{3,2,0}^{(URR)}\) decreased little by little, until it reached 50% when node 5's traffic load reached 4 packets/s.