A DTN routing strategy based on neural networks for urban bus transportation system

Routing in Delay/Disruption Tolerant Networks (DTNs) is a challenge because it must deal with possible unconnected end-to-end paths at the time of sending a message to a destination. An efficient selection of a contact node to forward a message is a key in the routing process. Prediction techniques can be used to assist in routing decisions. In this work we present a multi-copy routing strategy for a DTN built on the top of an Urban Bus Transportation System (UBTS). Using the buses as nodes, the contact history of nodes can be used to improve the DTN communication. The strategy relies on a journey predictor to build a multi-graph of predicted journeys to the destinations. It calls a next node contact predictor based on Artificial Neural Networks (ANNs) to compute each vertex of the multi-graph. A minimal delay criterion is applied to select the best journey. A copy control algorithm is used to improve the performance of the system. Experiments were carried out with real contacts of a quasi-opportunist scenario. The proposal outperformed the MaxProp strategy in the most cases considering the number of delivered messages, the delivery delay and the delivery cost.