Optimal Cellular Traffic Offloading Through Opportunistic Mobile Networks by Data Partitioning

In cellular traffic offloading through opportunistic mobile networks, existing schemes rely on the assumption that data can be entirely transmitted at each contact. However, transmission probability exponentially decreases as data size increases. That is, the contact duration in each contact might be insufficient for delivering large data. The objective of this paper is to find an optimal traffic offloading scheme through data partitioning so that the data delivery latency is minimized. There is a tradeoff in data partitioning. Each small chunk in a path has a higher delivery probability than original data, and consequently, has a shorter delivery latency under the persistent transmission model with re-transmission. However, the destination needs to receive all the chunks in multiple paths to retrieve the data. A delay in any path will lead to a longer delivery latency. We formulate the optimal cellular traffic offloading problem and propose an approach to generate forwarding paths with possible heterogeneous data chunks. Specifically, we discuss the optimal solution for single-hop direct forwarding with multiple offloading helpers and optimal chunk sizes. Then, we propose using the node’s social-feature to generate multiple edge-disjoint multi-hop forwarding paths. Extensive experiments on realistic traces show that our scheme achieves a much better performance than those without partitioning.