Evaluating Collaborative and Autonomous Agents in Data-Stream-Supported
  Coordination of Mobile Crowdsourcing
Ralf Bruns Jeremias Dötterl Jürgen Dunkel Sascha Ossowski
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.