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Data transfer in opportunistic Delay Tolerant Networks (DTNs) must rely onunscheduled sporadic meetings between nodes. The main challenge in thesenetworks is to develop a mechanism based on which nodes can learn to makenearly optimal forwarding decision rules despite having no a-priori knowledgeof the network topology. The forwarding mechanism should ideally result in ahigh delivery probability, low average latency and efficient usage of thenetwork resources. In this paper, we propose both centralized and decentralizedsingle-copy message forwarding algorithms that, under relatively strongassumptions about the networks behaviour, minimize the expected latencies fromany node in the network to a particular destination. After proving theoptimality of our proposed algorithms, we develop a decentralized algorithmthat involves a recursive maximum likelihood procedure to estimate the meetingrates. We confirm the improvement that our proposed algorithms make in thesystem performance through numerical simulations on datasets from synthetic andreal-world opportunistic networks.

Optimal Forwarding in Opportunistic Delay Tolerant Networks with Meeting Rate Estimations