Privacy-Aware Sensing-Quality-Based Budget Feasible Incentive Mechanism for Crowdsourcing Fingerprint Collection

Abstract

Mobile crowdsourcing (MCS) has shown great potential in received signal strength (RSS) fingerprint collection, in which an incentive mechanism plays a critical role to motivate users' participation. However, how to quantify the quality of the gathered fingerprint data is still not addressed well in the design of incentive mechanism for MCS-based fingerprint collection. In this paper, a sensing quality metric is proposed to characterize the joint impact of users' privacy protection and the spatial coverage of the submitted data. Given a limited budget, a basic incentive mechanism is devised to recruit appropriate users to maximize sensing quality. Considering that the cost of each user is regarded as private information and users may be attempted to misreport their costs to increase the revenue. Hence, an auction-based incentive mechanism is proposed to achieve the truthfulness of users' costs, which is truthful, individually rational, computationally efficient and budget feasible. Simulation results show that our proposed schemes outperform the baseline schemes and the experiment with real-world data is carried out to evaluate the performance of our proposed basic incentive mechanism. INDEX TERMS Local differential privacy, incentive mechanism, auction theory, crowdsourced fingerprint collection.

Quality Estimation for Scarce Scenarios Within Mobile Crowdsensing Systems

“…The advantage of opportunistic sensing is that it involves low user involvement [25], and thus is cheaper than participatory as less incentives, in particular monetary incentives, need to be provided to the participant. However, it could compromise the privacy and security of the participants or consume their resources [73,74]. Thus, opportunistic tasks need to be designed taking privacy, security, and resource consumption into consideration…”