Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

arXiv:2306.04366v2 Announce Type: replace-cross
Abstract: Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, …

abstract arxiv collaborations collaborative crowdsourcing cs.ai cs.hc cs.lg cs.si efficiency evaluation graph graph neural network mobile network neural network platforms recruitment relationships sensing social tasks teams trust type values workers