CBDTF: A Distributed and Trustworthy Data Trading Framework for Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) has emerged as a new sensing paradigm that relies on the sensing capabilities of the crowd to aggregate data. Unlike traditional MCS systems, where sensing data are traded via a third-party sensing platform, we propose a distributed data trading framework and investigate the potential of consortium blockchain to ensure the privacy and security of data transactions in MCS systems. The interactions between selling mobile users (SMUs) and buying mobile users (BMUs) are modeled as a Stackelberg game. Then, the amount of sensing time to purchase from each SMU and the price per unit sensing time are determined according to two auto-executing smart contracts. Notably, SMUs are compensated according to not only the amount of sensing time but also their reputation so that SMUs are encouraged to contribute high-quality data. Furthermore, the distributed ledger technology guarantees that the reputations of SMUs are updated and recorded in an immutable and traceable manner. Experimental results confirm that the proposed mechanism achieves near-optimal social welfare without requiring SMUs to know the price and data quality of each other.