Applying Machine Learning to Reduce Overhead in DTN Vehicular Networks

VANETs benefit from Delay Tolerant Networks (DTNs) routing algorithms when connectivity is intermittent because of the fast movement of vehicles. Multi-copy DTN algorithms spread message copies to increase the delivery probability but increasing network overhead. In this work we apply machine learning algorithms to reduce network overhead by discriminating the worst intermediate nodes for the transmission of copies. The scenario is a VANET of public buses that follow specific routes and schedules. This repetitive behavior creates an opportunity for applying trained classifiers to predict the occurrence of performance-related events. As the main contribution, our method decreases overhead without degrading delivery probability.