# An Energy-Saving Forwarding Mechanism Based on Clustering for Opportunistic Networks
## 3 Energy Impact  
The battery level is the remaining energy in the user's device used by the user's applications and communication among nearby devices. However, when this resource is low, we assume the nodes will not cooperate in opportunistic communication. Network metrics can degrade if a node leaves the network operations, as fewer nodes store the messages. To test this hypothesis, we carried out a questionnaire to evaluate two issues:  
1. the behavior of real users when taking part in OppNet at a critical energy level,
2. from which level of energy users consider taking part in the network only favoring themselves (not accepting messages from other users). We conducted this experiment on the UFAM campus Manaus with 351 participants of several courses to answer these questions. The research questions and alternatives can be seen in Table 1.  
The questionnaire result showed that 77.16% of the users would not accept the request to take part or wait for their batteries to recharge. This result corroborates the null hypothesis that nodes may leave network operations of storing data to forward later. One can analyze the cooperation behavior by using the second question as a guide. We found that 68.10% of the users would no longer cooperate in communication if the battery was lower than 30%. Only 19.65% of the users agreed to cooperate with the network operation if their energy level was above 50%. This result reinforces the hypothesis that low energy nodes would not cooperate with the network forwarding mechanism. We applied another experiment that highlights the impact of cooperation based on the energy level. This analysis can allow us to choose an appropriate design of experiments; it also defines an adequate set of investigation factors. A performance evaluation must comprise two key components, the design of experiments, which refers to planning the experiments to collect the data feasibly for statistical analysis, and the actual statistical analysis of the data ([PERSON] et al., 2018). We applied a 2k factorial design, a technique comprising two or more factors as input, representing some of the network parameters that significantly influence the performance metrics. Factorial design helps sort out the factors according to their impact and points out how their interactions can impact performance metrics. The basic approach for factorial design is to select a set of k factors with two levels (represented as \(-1\) and \(1\)). We simulated \begin{table}
\begin{tabular}{c c} \hline \hline
**Questions** & **Alternatives** \\ \hline (i) What do you do if someone sends you a contact request on an opportunistic network while your battery level is critical? & (a) accept the request (b) do not accept the request (c) wait for the battery to recharge \\ \hline (ii) What battery level would make you leave an opportunistic communication? & (a) below 10\% (b) between 10\% and 20\% (b) between 91\% and 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Questionnaire about user behavior in usage of their battery of mobile devices in OppNets. all combinations of k factors with 2k experiments. Since we were interested in the impact of energy level, we defined the factors in Table 2. Our response variable \(y\) is the probability of delivery of the messages. The coded variables were used in a regression non-linear model built to represent the effects of the factors and all their possible interactions on the variable, given by Equation (1). It is important to highlight that we carry out the factorial design without replication.