Redundancy-Aware and Budget-Feasible Incentive Mechanism in Crowd Sensing.

CROWDS; CROWDSENSING

Crowd sensing has emerged as a compelling paradigm for collecting sensing data over a vast area. It is of paramount importance for crowd sensing systems to provide effective incentive mechanisms. This paper studies the critical problem of maximizing the aggregate data utility under a budget constraint in incentive mechanism design in crowd sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behavior, and private cost information of smartphones users. Most of existing mechanisms do not consider the important performance objective—maximizing the redundancy-aware data utility of sensing data collected from smartphones users. Furthermore, they do not consider the practical constraint on budget. In this paper, we propose an incentive mechanism based on a reverse auction framework. It consists of an approximation algorithm for winning user determination and a critical payment scheme. The approximation algorithm guarantees an approximation ratio for the aggregate data utility at polynomial-time complexity. The critical payment scheme guarantees truthful bidding. The rigorous theoretical analysis demonstrates that our mechanism achieves truthfulness, individual rationality, computational efficiency and budget feasibility. [ABSTRACT FROM AUTHOR]

Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)