# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 7 Conclusions and Future Works  
In this paper, we presented a new routing mechanism that groups nodes inside two clusters, the cluster of highest energy level nodes and the clusters based on the profile interest of each node. We performed a questionnaire and factorial design to confirm that the energy range used for nodes to cooperate on communication has a significant negative impact on the probability of delivery of the messages. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Battery Range 25–100** & **Average 1** & **Average 2** & **Average Module** \\ \hline PRoPHET \(\times\) GPDMI & 0.28978 & 0.19852 & 0.09126 \\ \(e\)GPDMI \(\times\) GPDMI & 0.32365 & 0.19852 & 0.12513 \\ \(e\)GPDMI \(\times\) PRoPHET & 0.32365 & 0.28978 & 0.03387 \\ \hline
**Battery Range 50–100** & **Average 1** & **Average 2** & **Average Module** \\ \hline PRoPHET \(\times\) GPDMI & 0.11035 & 0.10047 & 0.00988 \\ \(e\)GPDMI \(\times\) GPDMI & 0.32269 & 0.10047 & 0.22222 \\ \(e\)GPDMI \(\times\) PRoPHET & 0.32269 & 0.11035 & 0.21234 \\ \hline
**Battery Range 75–100** & **Average 1** & **Average 2** & **Average Module** \\ \hline PRoPHET \(\times\) GPDMI & 0.02669 & 0.02825 & 0.00156 \\ \(e\)GPDMI \(\times\) GPDMI & 0.31454 & 0.02825 & 0.28629 \\ \(e\)GPDMI \(\times\)PRoPHET & 0.31454 & 0.02669 & 0.28785 \\ \hline
**Battery Range 0–100** & **Average 1** & **Average 2** & **Average Module** \\ \hline PRoPHET \(\times\) GPDMI & 0.47052 & 0.32056 & 0.14996 \\ \(e\)GPDMI \(\times\) GPDMI & 0.32605 & 0.32056 & 0.00549 \\ \(e\)GPDMI \(\times\) PRoPHET & 0.32605 & 0.47052 & 0.14447 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Tukey test applied to the ANOVA result obtained in Table 10.  
The results showed that \(\epsilon\)GPDMI can mitigate the degradation of messages delivery probability when nodes are unwilling to cooperate due to their energy level. It is reasonable to assume that this happened due to the formation of groups composed of nodes with similar energy levels within a battery range considered by the nodes as high for cooperation in the network. \(\epsilon\)GPDMI had a general performance that was better than PRoPHET and GPDMI in higher energy level restriction scenarios, especially when the battery range for nodes cooperation was \(E=\)[75, 100] (only nodes with high energy level cooperated). In addition, by applying the \(t\)-test, no evidence of a significant difference was found among \(\epsilon\)GPDMI, GPDMI, and PRoPHET in the average delivery delay metric, even with a better performance of \(\epsilon\)GPDMI in message deliveries. The current work has a wide range of uses in OppNets. Since there is a lack of studies that consider battery a critical parameter for routing strategy implementations, it is relevant to design protocols and other algorithms to balance energy usage, the nodes cooperation, and the messages delivery probability. As for future work, we intend to collect accurate data on human mobility with a history of energy consumption and battery percentages of users' mobile devices to evaluate the \(\epsilon\)GPDMI assessment experiments, applying machine learning techniques to learn about current energy levels of nearby devices. Besides, additional factors must be considered, such as time for recharging the battery, TTL of messages, and contacts duration. [PERSON], [PERSON], [PERSON] conceived the idea; [PERSON], [PERSON], [PERSON] designed and implemented the protocol optimization; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] analyzed the data. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. This research, according to Article 48 of Decree n\({}^{\circ}\) 6.008/2006, was partially funded by Samsung Electronics of Amazonia Ltda, under the terms of Federal Law n\({}^{\circ}\) 8.387/1991, through agreement n\({}^{\circ}\) 003/2019, signed with ICOMP/UFAM.