# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 10 Simulation Results and Analysis
### Impact of the Algorithm Tuning Parameters  
In this section, we show the results of changing the algorithm tuning parameters. In the ANLPP algorithm, we used two parameters, which are probability limit values. The minThreshold parameter makes sure the lowest node to charge is within a certain range. The maxThreshold parameter makes sure the highest node in the charging list is within a certain range. Figure 5 shows the impact of minThreshold, and Figure 6 shows the impact of maxThreshold.  
Figure 5 shows that, for the best performance of ANLPP, the minThreshold needs to reach a sufficiently high value. Increasing minThreshold causes ANLPP to reach a peak performance and then stabilize. Low values of minThreshold prevent the algorithm from selecting nodes to charge in favour of finding very low power nodes. This leads to either selecting nodes that actually could be dropped, or exhausting most of the MCR power while searching. This in turn reduces the performance of the algorithm, while further increase in minThreshold has a smaller impact, due to maxThreshold becoming the driving factor when minThreshold is too high.  
Figure 6 shows that maxThreshold has an optimal value for best possible performance of ANLPP, where an increase or decrease leads to degraded performance. A low value of maxThreshold leads to low performance for the same reasons a low value of minThreshold does. A high threshold value also leads to decreasing the algorithm performance. This is due to the algorithm terminating sooner than it should, after finding nodes with high probability of existence, leading to less exploration. In addition, a high threshold causes the algorithm to not pick low power nodes; hence, it is not able to extend the life of the network to high levels.  
Figure 5: The impact of minThreshold on the performance of All Network Least Possible Probability (ANLPP).  
Figure 6: The impact of maxThreshold on the performance of All Network Least Possible Probability (ANLPP).  
Figure 7 shows the performance of both Minimum Only and ANLPP after having minThreshold and maxThreshold tuned for each network size. We tune both minThreshold and maxThreshold numerically offline for both algorithms. The offline simulations used similar network parameters as the networks used to evaluate the algorithms. After tuning Minimum Only, we notice increases in mean, median and a shrinking variance, and Table 3 summarizes the performance improvements for each network size. The improvements in the mean performance are almost always positive with an increase varying between +2% to +21%. Variance shows a fluctuation between narrowing and widening, or even no change. It can be seen that tuning improves at least two performance indicators of the algorithm, leading to either higher overall performance by increases in mean or better performance predictability with decreases in variance. While tuning improves the performance of Minimum Only, we notice an even higher improvement when tuning ANLPP. Even though the algorithm parameters are static, this still shows the algorithm's flexibility to deal with variations in network parameters.