An Optimal Joint Scheduling and Drop Policy for Delay Tolerant Networks
Résumé
Delay Tolerant Networks are wireless networks where disconnections may occur frequently due to propagation phenomena, node mobility, and power outages. In order to achieve data delivery in such challenging networking environments, researchers have proposed the use of store-carry-and-forward protocols: there, a node may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. Multiple message replicas are often propagated to increase delivery probability. This combination of long-term storage and replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and drop policies are necessary to: (i) decide on the order by which messages should be replicated when contact durations are limited, and (ii) which messages should be discarded when nodes' buffers operate close to their capacity. In this paper, we propose an efficient joint scheduling and drop policy that can optimize different performance metrics, such as the average delivery ratio and the average delivery delay. Using the theory of encounter-based message dissemination, we first propose an optimal policy based on global knowledge about the network. Then, we introduce a distributed algorithm that uses statistical learning to approximate the global knowledge required by the optimal policy, in practice. Using simulations based on a synthetic mobility model and a real mobility trace, we show that our policy based on statistical learning successfully approximates the performance of the optimal policy in all considered scenarios. At the same time, both our optimal policy and its distributed variant outperform existing resource allocation schemes for DTNs, such as the RAPID protocol RAPID, both in terms of average delivery ratio and delivery delay.
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