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.

移动众包是指任务的完成必须要求众包人员在按需工作力量中进行物理移动的系统。本文提出了在移动众包中实现任务转移、结果预测和任务协调的不同机制，包括数据流学习方法用于任务结果预测，以及具有不同自治程度的两种不同方法实现任务协调，分别为具有协作但非自治的众包运输机制和具有自治众包人员的市场模式。