# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 6 Performance Evaluation
### Scenario Infocom  
Figure 3 shows the message delivery probability in each battery range for each tested routing algorithm. When applying the clustering layer for energy level over the other decision-making factors of the routing algorithms, we intended to keep messages alive, carried by nodes with the highest level, thus, mitigating the delivery rate degradation. Theresults confirm this intuition, since \(e\)GPDMI achieved the best message delivery probability when nodes became less cooperative to save energy.  
While in the base case \(E=[0,100]\), where all nodes cooperate regardless of the battery level, our approach had a message delivery rate of approximately 70%. The other routing algorithms had about an 85% delivery rate. Regarding the probability of delivery of the message of \(e\)GPDMI, a reasonable justification for this result relates to the fact that \(e\)GPDMI groups the nodes in two groups, energy levels and interests. As there was no restriction on energy level, only interests were taken into consideration. Thus, for PRoPHET and GPDMI, the flooding was higher since there was no restriction on energy. Hence, the number of replicated messages on the network was higher when compared to \(e\)GPDMI, as can be seen in Figure 5. Even with a smaller number of replicated messages (142.85% lower), \(e\)GPDMI achieved a probability of delivery only 19.35% lower than the other two protocols.  
When nodes with less than 75% battery level did not cooperate (\(E=[75,100]\)), \(e\)GPDMI had about an 80% message delivery rate, while the other routing algorithms had a delivery rate below 25%. The reasoning is that these algorithms decide to forward for nodes most likely to have new contacts, not necessarily the nodes with the highest battery levels.  
It is important to highlight that \(e\)GPDMI had a better performance (approx. 104.76%) in message delivery probability when compared to GPDMI and PRoPHET. In the result of the average delay (Figure 4), \(e\)GPDMI performed better in case \(E=[75,100]\). Therefore, to verify if there was a significant difference between the means, we applied the \(t\)-test, whose result demonstrates that both \(e\)GPDMI and GPDMI did not present a statistically significant difference (\(p\)-value = 0.3904). Similarly, the results comparing \(e\)GPDMI and PRoPHET (\(p\)-value = 0.3505) did not reach sufficient conditions to reject the null hypothesis. In summary, as shown in Figure 3, the probability of delivery was higher for the case \(E=[75,100]\) without substantially increasing the average delivery delay of \(e\)GPDMI with regard to the other routing protocols, as can be seen in Figure 4.  
Figure 3: Impact of battery ranges on the delivery probability of messages in the Infocom mobility scenario.  
In order to measure overhead, we defined overhead as follows:  
\[\text{overhead}=\frac{(\text{\#relayed\_msg}-\text{\#delivered\_msg})}{(\text{ \#delivered\_msg})} \tag{9}\]  
As shown in Figure 5, the overhead of our proposal (eGPDMI) is significantly lower in case \(E=[0,100]\). The results show that messages were relayed mostly to the nodes with the highest energy level, even if the nodes cooperated with a low battery level. Further, in this scenario, eGPDMI forwarded fewer messages than the other algorithms.  
On the other hand, when the battery load limit to forward increased, we noticed that eGPDMI relayed more messages than GPDMI and PRoPHET. These results imply that eGPDMI can keep messages alive longer than GPDMI and PRoPHET.  
Figure 4: Average delivery delay vs. battery ranges in scenario Infocom.  
Figure 5: Network overhead vs. battery ranges in Infocom scenario.