RBCrowd: A Reliable Blockchain-Based Reputation Management Framework for Privacy Preservation in Mobile Crowdsensing

Mobile crowdsensing (MCS) is an emerging paradigm that enables cost-effective, large-scale, and participatory data collection through mobile devices. However, the open nature of MCS raises significant privacy and trust challenges. Existing reputation models have made progress in assessing the quality of contributions, but they still struggle to manage prolonged inactivity, which can lead to outdated scores that no longer reflect current engagement. To address these issues, this paper presents RBCrowd, a dynamic reputation management system based on a dual blockchain architecture. It consists of the Sensing Chain (SC), a public blockchain recording sensing tasks and results, and the Reputation Chain (RC), a consortium blockchain managing user reputation scores. To guarantee privacy, the framework limits identity verification to the RC, ensuring that data on the SC is stored without direct links to the worker. We paired this privacy mechanism with a reputation model that rewards consistent, high-quality contributions. The system updates reputation scores by first validating the specific task and then adjusting for historical engagement, specifically penalizing prolonged inactivity. We evaluate RBCrowd through simulations in realistic MCS scenarios, and the results show that our framework provides more effective dynamic trust management than existing models. It also achieves increased reliability and fairness while managing prolonged inactivity through adaptive penalties.