  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 paper, we investigate two
different CMCS recruitment strategies allowing task requesters to form teams of
socially connected and skilled workers: i) a platform-based strategy where the
platform exploits its own knowledge about the workers to form a team and ii) 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:
level of expertise, social relationship strength, recruitment cost, and
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 meta-heuristic genetic
algorithm to select appropriate workers. Simulation results applied on a
real-world dataset 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.
