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Collaborative uploading describes a type of crowdsourcing scenario innetworked environments where a device utilizes multiple paths over neighboringdevices to upload content to a centralized processing entity such as a cloudservice. Intermediate devices may aggregate and preprocess this data stream.Such scenarios arise in the composition and aggregation of information, e.g.,from smartphones or sensors. We use a queuing theoretic description of thecollaborative uploading scenario, capturing the ability to split data intochunks that are then transmitted over multiple paths, and finally merged at thedestination. We analyze replication and allocation strategies that control themapping of data to paths and provide closed-form expressions that pinpoint theoptimal strategy given a description of the paths' service distributions.Finally, we provide an online path-aware adaptation of the allocation strategythat uses statistical inference to sequentially minimize the expected waitingtime for the uploaded data. Numerical results show the effectiveness of theadaptive approach compared to the proportional allocation and a variant of thejoin-the-shortest-queue allocation, especially for bursty path conditions.

Collaborative Uploading in Heterogeneous Networks: Optimal and Adaptive Strategies