DCrowd: Decentralized Mobile Crowdsensing Via Proof of Task Assignment Blockchain

Recently, blockchain-based decentralized mobile crowdsensing systems have emerged to eliminate traditional centralized trust and to achieve transparent task assignments via smart contracts. It allows workers to select tasks freely, thereby maximizing their benefits. However, prior designs rarely considered the globally optimal task assignment that significantly impacts the efficiency and quality of task performance, like maximizing the task completion ratio and minimizing the total travel distance of workers. So in this paper, we propose DCrowd, a new blockchain-based mobile crowdsensing system, to realize the decentralized, transparent, and globally optimal task assignment. In brief, we first introduce the Proof of Task Assignment consensus mechanism. This allows miners to conduct globally optimal task assignments off-chain, leverages smart contracts to perform lightweight verification for task assignment results on-chain, and stores the globally optimal task assignment in a customized block. Then, we devise the Weight-Prioritized Task Selection strategy and Threshold-based Adaptive Minimum Cost Flow algorithm, to further optimize the system performance and guide miners in competing for minting rights. A thorough theoretical analysis is provided. Extensive experiments on real-world datasets indicate that DCrowd can reduce the broadcast and consensus latency by over 50% and improve the throughput by over 87% compared with existing systems.