CrowdService: Optimizing Mobile Crowdsourcing and Service Composition

Some user needs can only be met by leveraging the capabilities of others to undertake particular tasks that require intelligence and labor. Crowdsourcing such capabilities is one way to achieve this. But providing a service that leverages crowd intelligence and labor is a challenge, since various factors need to be considered to enable reliable service provisioning. For example, the selection of an optimal set of workers from those who bid to perform a task needs to be made based on their reliability, expected reward, and distance to the target locations. Moreover, for an application involving multiple services, the overall cost and time constraints must be optimally allocated to each involved service. In this article, we develop a framework, named CrowdService, that supplies crowd intelligence and labor as publicly accessible crowd services via mobile crowdsourcing. The article extends our earlier work by providing an approach for constraints synthesis and worker selection. It employs a genetic algorithm to dynamically synthesize and update near-optimal cost and time constraints for each crowd service involved in a composite service and selects a near-optimal set of workers for each crowd service to be executed. We implement the proposed framework on Android platforms and evaluate its effectiveness, scalability, and usability in both experimental and user studies.