OPQR: Online Pricing and Quality Requesting for Mobile Crowd Sensing In mobile crowd sensing (MCS), appropriate rewards are always expected to compensate the participants for their consumptions of physical resources and involvements of manual efforts. Hence, the research about incentive mechanisms is essential and useful for MCS, and the existing works focus on optimizing one of the performance, such as the data quality, or the platforms profit, so, how to design an incentive mechanism which not only can guarantee the high data quality but also can maximize the profit of platforms is challenging issue. In this paper, we propose a qualitybased online incentive mechanism with unknown users cost distributions for MCS. The scheme uses a Markov Decision Procession (MDP) to online adjust the incentive price such that it tends to the optimal price. Moreover, the data quality request is also considered in our scheme. Furthermore, we evaluate the performances of our proposed scheme with single task and multiple tasks, extensive simulation results show that can perform better in profit and regret than the existing incentive scheme.

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.