## 4 Performance Evaluation
### Non-Clustered Network Performance Evaluation  
In the first simulation, a flat (non-clustered) network is considered where each node can communicate with the neighboring nodes within its transmission range. The result of this simulation is depicted in Figures 1-4. Figure 1 depicts an example of a deployed network, with the heatmap highlighting the amount of available energy in the deployment field, where x and y axis defining the size of the field. The figure shows the case with power control ON and OFF. Each case is shown with four subgraphs that are obtained as time evolves in the simulation. As can be observed, the nodes in the center of the network that usually relay more traffic deplete their energy faster than those nodes in the outskirts of the network. However, using adaptive power control, it can be observed that energy is distributed more uniformly than the case without power control. Figure 2 depicts the effect of power control on the percentage of inactive and disconnected nodes. Inactive nodes are defined as those nodes that ran out of power and are deactivated until they harvest enough energy to go back to sensing state. Meanwhile, disconnected nodes are defined as those nodes that have enough power to operate, however, they got disconnected from the rest of the network because of the broken links due to the inactive nodes. As can be observed from Figure 2a, when power control is ON, the network shows a slightly lower number of inactive nodes. The reason is that, by using power control, the nodes balance the energy consumption in a way that they try to reduce the traffic load on low power nodes indirectly forcing them to consume less energy. By increasing the number of nodes to 200, we can see more nodes that are running out of energy, where in round 1000, it reaches 9% and 13%, with and without power control, respectively. The reason behind this sharp increase in the number of inactive nodes can be due to longer hop routes from each source to \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes & 100, 200 \\ Network size & 1000 m \(\times\) 1000 m \\ Transmission range & Default = 100, \(\Delta\) = 150, \(\delta\) = 200 m \\ Power parameters & A = 50\%, \(\beta\) = 25\%, \(\theta\) = 10\% \\ Capacitor & 300 energy units \\ Packet rate & 30 packets per round \\ Energy harvesting rate & 6 units per round \\ Consumption rate & 1,3,10 units per round depending on transmission power \\ Noise Floor & 0, 10, 20, 30, 40 dB \\ Simulation run & 50 rounds \\ Confidence interval & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 1: Network power level heatmap demonstrating the changes in the nodes power level in four stages of the running network. destination, which causes more energy consumption. The effect of those inactive nodes on the network can be seen in Figure 2b, where the inactive nodes cause a part of the network to be disconnected from the rest. As it is shown earlier in Figure 1, nodes that are usually handling more traffic deplete their energy faster and run out of batteries. As those nodes are usually located in the central area and work as bridges between two sections of the network, their deaths cause network partitioning and disconnected nodes. However, as is shown, using adaptive power control, the network stays connected even in the presence of the same number of inactive nodes as nodes find new connections in the network since nodes can increase their transmission power. By increasing the number of nodes to 200, the network shows lower disconnected nodes due to more connections available that each node has. However, the pattern is still the same. Figure 4: Network performance in terms of (**a**) average remaining power per node and (**b**) average power consumed per delivered packet. Figure 3: Network performance in terms of (**a**) average packet delivery ratio and (**b**) average length of path.