Machine Learning Based Intelligent Routing for VDTNs

Delay Tolerant Networks (DTNs) are wireless mobile networks resilient to disruptions, significant latency, and incomplete paths between source and destination (S-D). Vehicular Delay Tolerant Network (VDTNs) is a subclass of DTNs. Most existing routing algorithms for VDTNs rely on predetermined rules that are inflexible to changes in node features (such as location, contact history, and node relationships). Moreover, given the extremely dynamic network architecture of the VDTN, the routing protocols must be flexible to the varying node features. Thus, the purpose of this work is to develop an intelligent routing system capable of determining the optimal routing method given a pair of S-D nodes. In this study, a novel self-adaptive routing method for VDTNs based on machine learning (ML) is proposed. Using real-time features of S-D nodes, the proposed method selects the optimal routing method. Using the Opportunistic Network Environment (ONE) simulator based on a real-world taxi trace, we evaluated the performance of our ML-based self-adaptive routing method employing various ML models. Evaluation results show that our method achieves up to 34.34% greater success rate and up to 23.75% lower average message delivery delay than existing methods.