Acceptance-Aware Mobile Crowdsourcing Worker Recruitment in Social Networks

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment mode, i.e., socially aware MCS, has been proposed to effectively enlarge worker pool and enhance task execution quality, by harnessing underlying social relationships. In this paper, we propose and develop a novel worker recruitment game in socially aware MCS, i.e., Acceptance-aware Worker Recruitment (AWR). To accommodate MCS task invitation diffusion over social networks, we design a Random Diffusion model, where workers randomly propagate task invitations to social neighbors, and receivers independently make a decision whether to accept or not. Based on the diffusion model, we formulate the AWR game as a combinatorial optimization problem, which strives to search a subset of seed workers to maximize overall task acceptance under a pre-given incentive budget. We prove its NP hardness, and devise a meta-heuristic-based evolutionary approach named MA-RAWR to balance exploration and exploitation during the search process. Comprehensive experiments using two real-world data sets clearly validate the effectiveness and efficiency of our proposed approach.

Automated summary

With the rise of smart mobile devices, Mobile Crowdsourcing (MCS) has emerged as an innovative distributed computing paradigm. Socially aware MCS has been proposed to enlarge worker pool and enhance task execution quality through harnessing social relationships. This paper proposes a novel worker recruitment game, Acceptance-aware Worker Recruitment (AWR), in socially aware MCS, and uses a Random Diffusion model to accommodate task invitation diffusion over social networks. The experiments using real-world data sets validate the effectiveness and efficiency of the proposed approach.