# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 10 Simulation Results and Analysis
### Impact of the Network Parameters  
For all simulated WSN created, we used the parameters in Table 2. We now show the impact of these parameters on the performance of ANLPP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Network Size** & **Mean** & **Median** & **Variance** \\ \hline Minimum Only & 100 & +0.05 & +0.01 & \(-\)0.05 \\ Minimum Only & 150 & +0.02 & +0.015 & \(-\)0.05 \\ Minimum Only & 200 & +0.052 & +0.085 & +0.015 \\ Minimum Only & Max & +0.076 & +0.186 & \(-\)0.055 \\ ANLPP & 100 & +0.115 & +0.145 & +0.085 \\ ANLPP & 150 & \(-\)0.005 & +0.025 & 0 \\ ANLPP & 200 & +0.118 & +0.123 & \(-\)0.178 \\ ANLPP & Max & +0.213 & +0.26 & \(-\)0.196  
We explored the impact of varying \(d\), \(k\), localization time, \(r\), \(W\), \(E_{c}\), \(p\), and \(T\) on the performance of ANLPP. Some parameters show little to no effect on the algorithm performance. Changes in \(W\) and \(r\) almost have no impact on the algorithm performance. This is because these parameters only impact the value of \(E(X)\), and not the algorithm or its behaviour. Other parameters show varying impact on performance. We notice a range of parameter changes that leads to very little or no impact on performance. Table 4 shows the possible range of parameter change that maintains performance when tuned for parameter values. We focus on some parameters and omit others that act similarly; we focus on \(d\), \(k\), and localization time. The distance between the sensor nodes, \(d\), impacts the performance of ANLPP. Figure 8 shows the impact of \(d\) on ANLPP, when it is tuned for \(d=3\). It is clear that the algorithm peaks at \(d=3\), and the algorithm's performance degrades with the change in \(d\). However, with a small range of change (\(\pm 0.5\)), the algorithm performance almost stays the same. While \(d\) has the narrowest range of values, ANLPP may tolerate and still provide high performance, and it can still be tuned to work with a different value of \(d\) all together. Tuning ANLPP is done by setting the proper values of minThreshold and maxThreshold. Figure 9 shows the choice of a different value \(d=7\) and the impact of maxThreshold on performance. This tuning process is currently performed offline and assumes the parameter \(d\) does not change during execution. This assumption is reasonable for most of our network parameters, since none of the parameters are of dynamic nature in a given WRSN. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Optimal** & **Maintained** & **Maintained** \\  & **Performance** & **Performance** & **Performance** \\  & **Tuned Value** & **Start** & **End** \\ \hline \(d\) & \(3\times\Lambda_{m}\) & 2.4 & 3.7 \\ \(k\) & 5 nodes & 3 & 7 \\ localization time & 36 s & 18 & 54 \\ \(r\) & 1.625 mWH & 50 uWH & 4 mWH \\ \(W\) & 2.34 WH & 1.17 WH & 9.36 H \\ \(E_{C}\) & 25 WH & 19 & 37 \\ \(p\) & 0.5 & 0.43 & 0.63 \\ \(T\) & \(1\times E_{C}\) & \(0.9\times E_{C}\) & \(1.1\times E_{C}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ANLPP network parameter range tolerance. Figure 8: The impact of \(d\), the distance between nodes, on ANLPP performance, when tuned for \(d=3\). Another network parameter that ANLPP is less sensitive to with a slightly higher range is \(k\), and the number of nodes that may be dropped. While \(k\) is a constant when it comes to a given WRSN, we can show that ANLPP can tolerate a slight change in this value. This tolerance may enable the algorithm to deal with different settings without the need for reconfiguration, or enable the algorithm to still be utilized during transition phases of a WRSN. Figure 10 shows the impact of changes in \(k\) on ANLPP tuned for \(k=5\) on a WRSN. While a big increase or decrease of \(k\) leads to a degradation of ANLPP performance, a slight change leads to almost no impact. While ANLPP in Figure 10 is tuned for dropping five nodes, it performs at the same level when dropping four or six nodes.