CEDO: content-centric dissemination algorithm for delay-tolerant networks
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Intelligent Dynamical Buffer Scheduling Mechanism for Intermittently Connected Mobile Network
Wireless Personal Communications: An International Journal
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In order to achieve data delivery in Delay Tolerant Networks (DTN), researchers have proposed the use of store-carry-and-forward protocols: a node there may store a message in its buffer and carry it along for long periods of time, until an appropriate forwarding opportunity arises. This way, messages can traverse disconnected parts of the network. Multiple message replicas are often propagated to further increase delivery probability. This combination of long-term storage and message replication imposes a high storage and bandwidth overhead. Thus, efficient scheduling and drop policies are necessary to 1) decide on the order by which messages should be replicated when contact durations are limited, and 2) which messages should be discarded when nodes' buffers operate close to their capacity. In this paper, we propose a practical and efficient joint scheduling and drop policy that can optimize different performance metrics, such as average delay and delivery probability. We first use the theory of encounter-based message dissemination to derive the optimal policy based on global knowledge about the network. Then, we introduce a method that estimates all necessary parameters using locally collected statistics. Based on this, we derive a distributed scheduling and drop policy that can approximate the performance of the optimal policy in practice. Using simulations based on synthetic and real mobility traces, we show that our optimal policy and its distributed variant outperform existing resource allocation schemes for DTNs. Finally, we study how sampled statistics can reduce the signaling overhead of our algorithm and examine its behavior under different congestion regimes. Our results suggest that close to optimal performance can be achieved even when nodes sample a small percentage of the available statistics.