A Blockchain-Based Mobile Crowdsensing and Its Incentive Mechanism

Abstract

Mobile crowdsensing (MCS) has become a crucial paradigm for the efficient implementation of large-scale sensing tasks in smart cities. However, untrustworthy mobile users often contribute lower-quality data and attempt to manipulate reward distributions unfairly. These problems will indirectly make mobile users more passively participate in the sensing task. To address these challenges, we propose a novel MCS system model that combines the public blockchain and the consortium blockchain. To provide an effective data quality evaluation, we introduce an advanced Sybil-resistant account grouping method and employ an enhanced grouping truth discovery algorithm to evaluate data quality accurately. Additionally, we suggest an adjustable fair reward distribution mechanism based on the Shapley value to promote equitable reward distribution. The proposed model provides a more dependable and effective means of achieving high-quality services for society. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.