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Mobile crowdsourcing refers to systems where the completion of tasksnecessarily requires physical movement of crowdworkers in an on-demandworkforce. Evidence suggests that in such systems, tasks often get assigned tocrowdworkers who struggle to complete those tasks successfully, resulting inhigh failure rates and low service quality. A promising solution to ensurehigher quality of service is to continuously adapt the assignment and respondto failure-causing events by transferring tasks to better-suited workers whouse different routes or vehicles. However, implementing task transfers inmobile crowdsourcing is difficult because workers are autonomous and may rejecttransfer requests. Moreover, task outcomes are uncertain and need to bepredicted. In this paper, we propose different mechanisms to achieve outcomeprediction and task coordination in mobile crowdsourcing. First, we analyzedifferent data stream learning approaches for the prediction of task outcomes.Second, based on the suggested prediction model, we propose and evaluate twodifferent approaches for task coordination with different degrees of autonomy:an opportunistic approach for crowdshipping with collaborative, butnon-autonomous workers, and a market-based model with autonomous workers forcrowdsensing.

Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported Coordination of Mobile Crowdsourcing