Quasi Group Role Assignment With Agent Satisfaction in Self-Service Spatiotemporal Crowdsourcing

Abstract

Quasi group role assignment (QGRA) presents a novel social computing model designed to address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC), specifically for tackling the photographing to make money problem (PMMP). Nevertheless, the application of QGRA in practical scenarios encounters a significant bottleneck. QGRA provides optimal assignment strategies under conditions where both the number of crowdsourced tasks and workers remain stable. However, real-world crowdsourcing applications may necessitate the phased integration of new tasks. With the rapid increase in the number of tasks, a set of residual tasks inevitably exists that are difficult to complete. To maximize the completion of crowdsourced tasks, workers may be assigned low-yield or even unprofitable tasks. Given the reluctance of crowdsourcing workers to be overstretched for these tasks, along with the inherent characteristics of self-service crowdsourcing tasks, this can lead to the failure of the assignment scheme. To tackle the identified challenges, this article proposes the QGRA with agent satisfaction (QGRAAS) method. Initially, it sheds light on a creative satisfaction filtering algorithm (SFA), which is engineered to perform optimal task assignments while actively optimizing the profitability of crowdsourcing workers. This approach ensures the satisfaction of workers, thereby fostering their loyalty to the platform. Concurrently, in response to the phased changes in the crowdsourcing environment, this article incorporates the concept of bonus incentives. This aids decision-makers in achieving a tradeoff between the operational costs and task completion rates. The robustness and practicality of the proposed solutions are confirmed through simulation experiments.