Quasi Group Role Assignment With Role Awareness in Self-Service Spatiotemporal Crowdsourcing

Abstract

Self-service spatiotemporal crowdsourcing (SSC), a booming variant of spatiotemporal crowdsourcing (SC), emerges because of the vigorous development of the mobile Internet. Unlike the conventional SCs, the particularity of self-service in SSC may lead to unfinished tasks at the end of the entire assignment process, making a one-time assignment scheme ineffective. SSC is essentially an adaptive collaboration (AC) problem that requires a dynamic assignment strategy for a higher task completion rate. This article tackles this issue by establishing a quasi group role assignment (QGRA) based on a typical SSC scenario, that is, the photographing to make money problem (PMMP). First, it sheds light on a novel role awareness method, which can effectively divide tasks to accelerate the solution while, to some extent, raising the task completion rate. Second, it specifies an agent satisfaction evaluation (ASE) method to quantify the relationship between task completion rate and workers’ satisfaction. This method aims at considerably ameliorating task completion rate. Last, it extends QGRA with a new AC algorithm, which can achieve AC of the workers while accomplishing the crowdsourcing task. Moreover, utilizing the ASE method can help decision-makers balance the task completion rate and the workers’ satisfaction. Large-scale simulation experiments based on the real crowdsourced datasets exemplify the robustness and practicability of the proposed solutions. This article contributes a new version of the group role assignment (GRA) model, that is, quasi GRA (QGRA), a creative formalization to solve the AC problem.