# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Random Topologies  
Consider the scenario illustrated in Figure 13, where sensors at nodes 3, 4, and 5 send sensor measurements to the concentrator at node 0, and nodes 1 and 2 are available for routing packets. The difference between this and previous scenarios is that nodes 1, 2, 4, and 5 will at this stage be at random locations. More precisely, with node 0 located at (\(-\)80,0) and node 3 located at (80,0), node 1 will be uniformly distributed in the rectangular region with opposing vertices at (\(-\)10,\(-\)45) and (10,0); and node 2 will be uniformly distributed in the rectangular region with opposing vertices at (\(-\)10,45) and (10,0). The reason for placing nodes 1 and 2 at these rectangles is to create a scenario with 2 candidate routes; otherwise, if the random drops were such that only one route were available, all procedures would behave in the same way. Once nodes 1 and 2 are placed, as illustrated Figure 13, node 4 is placed 95 m below node 1 and node 5 is placed 95 m above node 2.  
Assume that nodes 3, 4, and 5 respectively generate an average of 20, 10, 0.5 packet/s. Although node 5 generates less traffic load than node 4, route 3,2,0 is not necessarily the best route because the random position of nodes can cause different hidden node problems, and we compare the procedures based on the amount of retransmissions per 1000 messages.  
We generated 30 sets of random locations for nodes 1 and 2, and for each set or random locations, we ran each of the three procedures using 30 different random simulation seeds.  
Figure 12: The median and the 15 th to 85 th percentiles of (**a**): the cumulative route cost of the routes 3,6,x,0 (\(\overline{x}_{3,6,x,0}\)), (**b**): the cumulative route cost of the routes 3,7,x,0 (\(\overline{x}_{3,7,x,0}\)), (**c**): the standard deviation of the estimated costs measured at node 3 for the routes 3,6,x,0 (\(\overline{x}_{3,6,x,0}\)), (**d**): the standard deviation of the estimated costs measured at node 3 for the routes 3,7,x,0 (\(\overline{x}_{3,7,x,0}\)), (**e**): the ratio of times that packets were relayed by the best routes 3,6,1,0 and 3,7,2,0 (\(\overline{x}_{3,6,1,0}+\overline{x}_{3,7,2,0}\)), and (**f**): the delivery rate of messages originated at nodes 3, 8, and 9 in the scenario A3.  
Figure 14a shows the cumulative distribution of the number of node 3 retransmissions per 1000 messages generated by each procedure. It is possible to see that, with the U-RR procedure, 55% of the scenarios ran showed node 3 with 100 or less retransmissions per 1000 messages generated; and, with the LS-based or LQI-based, only 35% of the scenarios showed node 3 with 100 or less retransmissions per 1000 messages generated.  
Figure 14b shows boxplots of the percentage reduction in the number of node 3 retransmissions per 1000 messages generated when compared to the LQI-based and the LS-based procedures. In each boxplot, the whiskers represent the 5 th and 95 th percentiles, the bottom and top parts of the box represent the 25 th and 75 th percentiles, and the line inside the box represents the median of the 30 random locations. When comparing against the LS-based and LQI-based procedures, the U-RR procedure reduced the amount of retransmissions by 14% or more in 25% of the random scenarios. In some scenarios, the reduction with respect to the LS-based procedure was 34%.