POMDP-Based Routing for DTNs with Partial Knowledge and Dependent Failures

TL;DR

This paper explores the use of POMDPs for routing in Delay-Tolerant Networks, addressing partial knowledge and dependent failures, and demonstrates improved performance in simulations.

Contribution

It introduces a novel POMDP model with dependent node failures for DTN routing and evaluates its effectiveness and scalability.

Findings

POMDP routing improves delivery ratios in DTNs.

The model handles correlated node failures effectively.

Scalable implementation maintains performance in simulations.

Abstract

Routing in Delay-Tolerant Networks (DTNs) is inherently challenging due to sparse connectivity, long delays, and frequent disruptions. While Markov Decision Processes (MDPs) have been used to model uncertainty, they assume full state observability - an assumption that breaks down in partitioned DTNs, where each node operates with inherently partial knowledge of the network state. In this work, we investigate the role of Partially Observable Markov Decision Processes (POMDPs) for DTN routing under uncertainty. We introduce and evaluate a novel model: Dependent Node Failures (DNF), which captures correlated node failures via repairable node states modeled as Continuous-Time Markov Chains (CTMCs). We implement the model using JuliaPOMDP and integrate it with DTN simulations via DtnSim. Our evaluation demonstrates that POMDP-based routing yields improved delivery ratios and delay performance under uncertain conditions while maintaining scalability. These results highlight the potential of POMDPs as a principled foundation for decision-making in future DTN deployments.