Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

Abstract

Collaborative mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this article, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: 1) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and 2) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its social network (SN) neighbors. We first formulate the recruitment as an integer linear program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: 1) level of expertise; 2) social relationship strength; 3) recruitment cost; and 4) recruiter’s confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on graph neural networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers’ search space and afterwards, exploiting a metaheuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world data set illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.