A workerselection incentive mechanism for optimizing platformcentric mobile crowdsourcing systems Abstract   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 multiattribute auction and reverse auction to select the candidate set of crowd workers dynamically. In order to further optimize the utility of platformcentric 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 workerselection incentive mechanism are verified. From experimental results, it can be inferred that the proposed workerselection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively.

Unknown Worker Recruitment with Budget and Covering Constraints for Mobile Crowdsensing Mobile crowdsensing, through which a requester can recruit a group of crowd workers via a platform and coordinate them to perform some sensing tasks, has attracted lots of attention recently. However, most of the existing mobile crowdsensing systems assume that the qualities of workers are known in advance. Based on this assumption, they study the task assignment and worker recruitment problems. Unfortunately, the qualities of workers are generally unknown in reality, so the platform must find the tradeoff between exploring and exploiting the qualities by using reinforcement learning. At the same time, all sensing tasks are required to be covered in each round (covering constraint), and the requester usually has a limited budget (budget constraint). In this paper, we study how to recruit unknown workers under the budget and covering constraints so that the total expected achieved qualities can be maximized. To this end, we model the problem as a combination of a maximum weight matching problem and a special multiarmed bandit problem. We first consider that the recruitment costs of workers are homogeneous and propose a recruitment algorithm with a performance guarantee. Then, we study the heterogenous case and devise a heuristic algorithm. Finally, we demonstrate the performances of our algorithms through extensive simulations.