Comparing Statistical and Analytical Routing Approaches for Delay-Tolerant Networks

In delay-tolerant networks (DTNs) with uncertain contact plans, the communication episodes and their reliabilities are known a priori. To maximize the end-to-end delivery probability, a bounded network-wide number of message copies are allowed. The resulting multi-copy routing optimization problem is naturally modelled as a Markov decision process with distributed information. The two state-of-the-art solution approaches are statistical model checking with scheduler sampling, and the analytical RUCoP algorithm based on probabilistic model checking. In this paper, we provide an in-depth comparison of the two approaches. We use an extensive benchmark set comprising random networks, scalable binomial topologies, and realistic ring-road low Earth orbit satellite networks. We evaluate the obtained message delivery probabilities as well as the computational effort. Our results show that both approaches are suitable tools for obtaining reliable routes in DTN, and expose a trade-off between scalability and solution quality.