# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Symmetric Topologies
#### 7.1.2 Scenario S2: WiFi interference  
Consider a symmetric scenario with WiFi interference. As illustrated in Figure 5, ZigBee nodes 0, 1, 2, and 3 are respectively at (0,80), (\(-\)35,0), (35,0), and (0,\(-\)80) and node 3 sends application data at an average rate of 20 packets/s to node 0 in the same communication channel as a WiFi IEEE 802.11n network. The WiFi network consists of one access point and two stations respectively, at (0,60), (10,60), and (\(-\)10,60). We assume that, at the time 100 s of the simulation, the WiFi access point starts two data streams of constant-bit-rate traffic of \(R_{WiFi}\) bits/s, one to each WiFi station; and these streams last until the end of the simulation at 400 s.  
In this topology, the WiFi devices are far from node 3 and may transmit while node 3 is transmitting, causing interference in the reception at nodes 1 and 2. As explained in Section 7.1.1, the symmetry of the topology means that the rates of interfering transmissions in nodes 1 and 2 are the same and the actual route costs for routes 3,1,0 and 3,2,0 satisfy \(c_{3,1,0}=c_{3,2,0}\).  
Figure 6a,b respectively, show the median and the 15 th to 85 th percentiles of \(\overline{c}_{3,2,0}\) and \(\sigma_{3,2,0}\) for 30 simulation runs at each WiFi application rate (\(R_{WiFi}\)) for the LS-based, the LQI-based, and the U-RR procedures. Since the WiFi traffic started only at \(t=100\) s, the \(\overline{c}_{3,2,0}\) and \(\sigma_{3,2,0}\) were obtained considering cost estimates after \(t=181\) s. The figures for the median of \(\overline{c}_{3,1,0}\) and \(\sigma_{3,1,0}\) showed similar behavior, and were therefore, omitted.  
As discussed previously, the LQI-based procedure had the lowest variation in all scenarios; but it was not able to detect the WiFi interference. Even when the WiFi traffic was consuming more than 20% of the channel time, \(\overline{c}_{3,1,0}^{(LQI)}=\overline{c}_{3,2,0}^{(LQI)}=2\) most of the time.  
Comparing the LS-based and the U-RR procedures, similarly to the analysis in the ZigBee-only network, it is possible to observe in Figure 6b that \(\sigma_{3,2,0}^{(URR)}\leq\sigma_{3,2,0}^{(LS)}\) in most traffic loads. The better performance of the U-RR procedure in comparison to the LS-based procedure was due to its reliance on unicast transmissions, which were more abundant than LS transmissions. It is also possible to observe the cyclic variations in \(\sigma_{3,2,0}^{(URR)}\) caused by the rounding of cost estimates discussed in the previous section. These results show that the U-RR procedure is able to reduce cost estimate variations even under WiFi interference.  
Figure 5: Topology where ZigBee devices share same spectrum as WiFi devices.  
It is interesting to note in Figure 6b that \(\sigma^{(LS)}_{3,2,0}\approx\sigma^{(URR)}_{3,2,0}\) when \(R_{WiFi}=600\) kbps. To understand this, we observed that the interference in the links between nodes 0, 1, and 2 caused by the WiFi transmissions when \(R_{WiFi}=600\) kbps caused both \(\hat{\rho}^{(LS)}_{1,0}(t)\) and \(\hat{\rho}^{(LS)}_{2,0}(t)\) to be below 0.627, which maps into the maximum link cost 7. This can be seen in Figure 6a, which shows \(\hat{\epsilon}^{(LS)}_{3,2,0}(t)\approx 8.7\) when \(R_{WiFi}=600\) kbps. This suggests that the high WiFi traffic caused the link cost estimates \(\hat{\epsilon}^{(LS)}_{1,0}(t)\) and \(\hat{\epsilon}^{(LS)}_{2,0}(t)\) to saturate at 7, reducing the standard deviation of the cost estimates over the course of the simulation.  
Also similar to the Scenario S1, all procedures choose the route 3,2,0 around 50% of the time and showed similar performance regarding retransmissions, as illustrated in Figure 6c,d. This behavior was expected, since \(c_{3,1,0}=c_{3,2,0}\).