Towards Redundancy-Aware Data Utility Maximization in Crowdsourced Sensing with Smartphones

Abstract

This paper studies the critical problem of maximizing the aggregate data utility under budget constraint in mobile crowd sourced sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behaviors, and private cost information of smartphones. Most of existing approaches do not consider the important performance objective - maximizing the redundancy-aware data utility of sensing data collected from smartphones. Furthermore, they do not consider the practical constraint on budget. In this paper, we propose a combinatorial auction mechanism based on a reverse auction framework. It consists of an approximation algorithm for winning bids determination and a critical payment scheme. The approximation algorithm guarantees a constant approximation ratio at polynomial-time complexity. The critical payment scheme guarantees truthful bidding. The rigid theoretical analysis demonstrates that our mechanism achieves truthfulness, individual rationality, computational efficiency, and budget feasibility. Extensive simulations show that the proposed mechanism produces high redundancy-aware data utility.