URIM: Utility-Oriented Role-Centric Incentive Mechanism Design for Blockchain-Based Crowdsensing

Crowdsensing is a prominent paradigm that collects data by outsourcing to individuals with sensing devices. However, most existing crowdsensing systems are based on centralized architecture which suffers from poor data quality, high service charge, single point of failure, etc. Some studies have explored decentralized architectures and implementations for crowdsensing based on blockchain, while incentive mechanisms for worker participation and miner participation, which serve as a crucial role in blockchain-based crowdsensing systems (BCSs), are ignored. To address this issue, we propose an incentive mechanism design named URIM to maximize participants’ utilities, which consists of worker-centric and miner-centric incentive mechanisms for BCSs. For the worker-centric incentive mechanism, we model it as a reverse auction, in which dynamic programming is utilized to select workers, and payments are determined based on the Vickrey-Clarke-Groves scheme. We also prove this incentive mechanism is computationally efficient, individually rational and truthful. For the miner-centric incentive mechanism, we model interactions among the requester and miners as a Stackelberg game and adopt the backward induction to analyze its equilibrium at which the utilities of the requester and miners are optimized. Finally, we demonstrate the significant performance of URIM through extensive simulations.