Towards to intelligent routing for DTN protocols using machine learning techniques

The communication protocols of wireless networks have experienced great advances in recent years, specifically with the evolution of new technologies such as the Internet of Things (IoT). However, certain problems remain unsolved, in particular for wireless networks, and more specifically for DTN networks, which represent a major challenge in terms of DTN routing. This paper aims to design an intelligent routing system based on machine learning techniques, the use of which represents another possibility to classify bundles that have arrived at the destination successfully or not. These networks occasionally carry out an evaluation which makes it possible to choose the type of routing corresponding to a given situation. It then minimizes the unnecessary information of the entries and performs the classification of the data. Despite the problems cited, our challenge is to design an intelligent routing mechanism that is able to classify bundles that have arrived and those that have not arrived at their destination. The smart routing system uses machine learning as a main tool to design our system. Indeed, various Machine Learning techniques, such as Bagging and Boosting, have been used to classify whether bundles have arrived at their destination successfully or not. Machine Learning now enables us to learn directly from data rather than human expertise, resulting in higher accuracy. We utilized the SMOTE technique to balance the two groups of data, which allows us to collect the equal amount of samples for each class. We also included techniques for interpreting complicated Machine Learning Models to understand the reasoning for model decisions, such as SHAP values. Results show an overall accuracy of 80% for the Random Forest (RF) and ExtraTrees Classifier (ET).