A worker-selection incentive mechanism for optimizing platform-centric mobile crowdsourcing systems

With the development of Mobile Crowd Sensing Networks (MCSN), more and more Mobile Crowdsourcing applications emerge. The mobile sensing technologies and theories have become the research hotspot. How to design an effective incentive mechanism to maximize social welfare is the key research in mobile crowdsourcing. This paper proposes an online auction algorithm through combining multi-attribute auction and reverse auction to select the candidate set of crowd workers dynamically. In order to further optimize the utility of platform-centric mobile crowdsourcing, the candidate set of crowd workers is optimized through improving the Discrete Particle Swarm Optimization (DPSO), which can determine the winner set of crowd workers. Through comparison experiments on simulation dataset and real dataset, the effectiveness and performance of the proposed worker-selection incentive mechanism are verified. From experimental results, it can be inferred that the proposed worker-selection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively. (C) 2020 Published by Elsevier B.V.