Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.

翻译：移动众包指必须依赖按需劳动力中众包工人实际物理移动才能完成任务的系统。证据表明，在此类系统中，任务往往被分配给难以成功完成任务的众包工人，导致失败率高、服务质量低。一个有望提升服务质量的解决方案是持续调整任务分配，通过将任务转移给使用不同路线或交通工具的更合适工人，从而对引发失败的事件做出响应。然而，在移动众包中实施任务转移存在困难，因为工人具有自主性，可能拒绝转移请求。此外，任务结果具有不确定性且需进行预测。本文提出多种机制以实现移动众包中的结果预测与任务协调。首先，我们分析用于任务结果预测的不同数据流学习方法。其次，基于所提出的预测模型，我们提出并评估两种不同自主程度的任务协调方法：一种针对众包运输的协作式但非自主工人的机会主义方法，以及一种面向众包感知的基于市场且包含自主工人的模型。