Vehicle Delay-tolerant Network Routing Algorithm based on Multi-period Bayesian Network

Abstract

Delay-tolerant networks (DTNs) are wireless mobile networks where constant end-to-end connections may not exist among nodes. In real-life vehicle DTNs, most nodes have repetitive movement patterns. However, due to the change of time and different activity scenarios, the movement patterns cannot be described consistently with a single model. Considering this issue, the Multi-period Bayesian Network (MBN) is proposed to build multiple prediction models, which intends to predict the regular movement patterns of nodes in the real world. The Bayesian network model is constructed by using several network parameters (e.g. spatial and temporal information at the time of message forwarding) to describe the movement patterns of DTN nodes. Additionally, a novel classification method called Dynamic Multiple-Level Classification (DMLC), is proposed where nodes are classified into multiple levels according to the dynamic parameters. Followed by that, a routing algorithm based on MBN is presented, which can make routing decisions based on the classification results of DMLC. The simulation results show that MBN algorithm and DMLC method can improve the delivery ratio with a minor forwarding overhead.