## 5 Discussion  
Nowadays, OppNet routing algorithms cannot be objectively evaluated nor compared because there is a lack of a globally accepted evaluation methods. This situation hinders the development of new routing algorithms. The present proposal intends to contribute toward an objective evaluation methodology by providing an analytically selected collection of scenarios, a corpus. This proposal will help ensure that evaluation results can be reliable, reproduced and contrasted in order to improve the objective quality. Researchers have tried to evaluate their proposals fairly, for example, by evaluating each other's proposals, using scenarios that other researchers have used, or selecting metrics that fit their proposals. However, these evaluation approaches have not overcome problems such as lack of reproducibility or inability to generalize routing algorithms to any scenario. It is common practice in OppNets to use well-known scenarios with a clear intention of standardizing evaluation methods. The problem, though, is not just a matter of using the same scenarios. If the routing algorithm being evaluated has to be general-purpose, it is also a requirement that the scenarios being used are representative of all possible network situations. Therefore, any collection of scenarios is not the solution, and what is needed is a fine selection of representative scenarios. Besides the existence of a representative corpus, it is as well important that it be used by the community. The corpus introduced in this study has been proven as representative by means of experimentation, and has been made publicly available. This work is not intended to create a dilemma of whether or not the corpus should replace the well-known scenarios. Obtaining a simple corpus is not a difficult task. There are different methods of obtaining a collection of scenarios in a straightforward manner, for example by using classical programming techniques such as random selection, trialand error, genetic algorithms, or even machine learning approaches. However, obtaining a representative corpus is complex and challenging. A representative one represents, as a whole, all possible network behaviors. The selection of scenarios for a representative corpus goes beyond a cherry-picking process, and each scenario is carefully analyzed and compared with other scenarios. Still, the selection process may not matter as much as the corpus itself. The differences and representativeness of network behavior that the corpus has are what determines if a corpus is useful or not. The corpus presented in this work was obtained via a creation methodology based on identifying the variables that characterize OppNet scenarios, methods to create OppNet scenarios and processes to assess differences and diversity among them. The differences and the representativeness of each scenario were carefully assessed. The results measured the representativeness and diversity of the corpus scenarios, showing significant differences. Therefore, it can be said that this is a representative corpus for objective evaluation. Having a representative corpus does not imply necessarily that it is the best. The scenarios of the corpus should be reviewed in the future, especially as new technologies emerge from arising new network behaviors. The corpus comprises simple scenarios where network behaviors are uniform. There might be environments where it is interesting to have non-uniform behaviors, for example, when defining strategies where the routing algorithm changes depending on network conditions. These complex scenarios can be built, for instance, by concatenating simple scenarios from the corpus without unnecessarily expanding the number of scenarios in the corpus. When there is a corpus, there is the risk of falling into the trap of developing tailored solutions that only work with the elements of this corpus.