# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 6 Performance Evaluation
### Realistic Environment Evaluation  
In order to evaluate the proposed RPF in a realistic environment, a solar radiation dataset for the city of Corfu in Greece between July 1983 and July 2017 was obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program [6]. Furthermore, network nodes specifications are based on the datasheet of MICA2 (MPR400 CB) [61]. Similar parameters regarding the simulation scenarios as for the previous cases are considered (e.g., random geometric graph topologies). The network nodes' and the solar panel's parameters are drawn in Tables 3 and 4, respectively. Each node recharges its battery with a probability of 0.8 and generates data packets that are forwarded towards the sink node, with a probability of 0.01. Along with these packets, sink node generates a data packet which is disseminated to the network using PF or RPF. Nodes' batteries discharge during the night and when the remaining energy falls below 10%, they stop operating. When the sun rises, nodes' batteries become charged by the insolation on the solar panel and when the remaining energy climbs above 20% nodes become operational again. At the time that the sink node starts to disseminate the information message in the network, a fraction of nodes may be in a non-operational state, resulting in a reduced coverage under PF. \begin{table}
\begin{tabular}{l l} \hline \hline Processing current draw & 400 mA \\ Transmit current draw & 27 mA \\ Receive current draw & 10 mA \\ Battery capacity & 100 mAh \\ Data rate & 38.4 kbps \\ Data packet size & 50 bytes \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Node parameters. Figure 9: Termination time \(\mathcal{T}\) under RPF for three different sets of \(\alpha\), \(\beta\) and \(\gamma\) for \(r_{c}=0.10\), 0.20 and 0.30. Line “no energy harvesting” corresponds to the case that no energy harvesting is activated in the network. Figures 10 and 11 depict simulation results regarding coverage at termination time under PF and RPF, respectively, as a function of initially operating nodes for three geometric random graph topologies with connectivity radius \(r_{c}=0.1\), 0.2 and 0.3 and three different forwarding probabilities. It is observed that PF coverage keeps low when the number of initially operating nodes is small, even for large values of the forwarding probability. For example, in Figure 10a and for the highest value of the forwarding probability, coverage is high (>0.8) for over 650 initially operating nodes. However, in Figure 11, it is observed that RPF coverage, for the same topologies and forwarding probabilities, remains high even for the smallest number of initially operating nodes. Under the proposed RPF, the node that plans to forward the information message waits for its neighbors to become operational. This behavior results in an increased termination time of the proposed Algorithm 1. The latter is observed in Figures 12 and 13, which depict simulation results regarding termination time under PF and RPF, respectively, as a function of initially operating nodes for three geometric random graph topologies with connectivity radius \(r_{c}=0.1\), 0.2 and 0.3 and three different forwarding probabilities. It is observed that the termination time of the RPF is significantly larger than this of the PF in all cases. For example, for the case of the geometric random graph with connectivity radius \(r_{c}=0.2\) (i.e., Figures 12b and 13b) with 500 initially operating nodes and for \(q=0.06\), the termination time under PF is 25, while, under RPF, it is 62. When all the nodes of the network are operational, there are no additional delays under RPF and thus the proposed Algorithm 1 reduces to PF. The latter is observed for all cases where the number of initial operating nodes equals 1000.