Task Execution Quality Maximization for Mobile Crowdsourcing in Geo-Social Networks

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With the rapid development of smart devices and high-quality wireless technologies, mobile crowdsourcing (MCS) has been drawing increasing attention with its great potential in collaboratively completing complicated tasks on a large scale. A key issue toward successful MCS is participant recruitment, where a MCS platform directly recruits suitable crowd participants to execute outsourced tasks by physically traveling to specified locations. Recently, a novel recruitment strategy, namely Word-of-Mouth(WoM)-based MCS, has emerged to effectively improve recruitment effectiveness, by fully exploring users' mobility traces and social relationships on geo-social networks. Against this background, we study in this paper a novel problem, namely Expected Task Execution Quality Maximization (ETEQM) for MCS in geo-social networks, which strives to search a subset of seed users to maximize the expected task execution quality of all recruited participants, under a given incentive budget. To characterize the MCS task propagation process over geo-social networks, we first adopt a propagation tree structure to model the autonomous recruitment between the referrers and the referrals. Based on the model, we then formalize the task execution quality and devise a novel incentive mechanism by harnessing the business strategy of multi-level marketing. We formulate our ETEQM problem as a combinatorial optimization problem, and analyze its NP hardness and high-dimensional characteristics. Based on a cooperative co-evolution framework, we proposed a divide-and-conquer problem-solving approach named ETEQM-CC. We conduct extensive simulation experiments and a case study, verifying the effectiveness of our proposed approach.