Mobile crowdsourcing (MCS) endeavors to attain reliable truth by recruiting large numbers of users with handheld mobile devices to collect the data. However, during the early stages of platform development, MCS encounters the cold start problem, failing to complete the task. Existing research addresses this issue by leveraging social networks for user recruitment. Nevertheless, there is a predominant focus on the user quantity, and the quality of task completion is ignored. Additionally, fairness considerations among users are lacking. Therefore, this article proposes recruitment based on social users’ trust (RSUT) to solve the cold start problem while maintaining high task completion quality. Specifically, we propose the activation model based on the user awareness to simulate the influence of social users and task attributes on activation from the perspective of unregistered users, which is more realistic. Additionally, we measure the user’s contribution and then design a reward system based on the user’s contribution to ensure fairness. Finally, social network-based trust evaluation is proposed to identify malicious users and update rewards in real time according to task requirements to ensure high-quality completion of tasks within budget constraints. Extensive experimental results demonstrate the superior performance of RSUT compared to the state-of-the-art methods in task completion quality, user recruitment, and task completion rate.

Mobile crowdsourcing aims to recruit enough workers holding mobile devices to collect data. Nevertheless, the platform will have cold start problems when the number of workers is limited. Existing studies have proposed solving this problem by propagating tasks to social networks for social recruitment. However, they neglect to verify workers’ propagation, leading to malicious workers reducing the platform's utility. Furthermore, during propagation verification, it is imperative to protect the privacy of social relationships among workers, as it can significantly influence the propagation. Therefore, this paper proposes Zero-knowledge Propagation Verification based on Social Relationship Encryption (ZPV-SRE) to improve the platform's utility. Specifically, we transform the propagation verification problem into a problem of computing the solution of the function. Then, the Zero-knowledge proof is used to prove the propagation, in which the worker's social relationship is protected through homomorphic encryption. Considering that ZPV-SRE will incur a significant time cost, we propose Trust-guided Zero-knowledge Propagation Verification based on Social Relationship Encryption (TZPV-SRE), which updates the worker's trust based on the verification results and selects suspicious workers for verification. The experimental results show ZPV-SRE improves the platform's utility as high as 104.05% over the state-of-the-art methods, while TZPV-SRE reduces time costs and ensures improvement.

Incentive mechanisms are essential to incentive workers carrying mobile handheld devices to participate in mobile crowd sensing and finally achieve good truth discovery performance. However, malicious workers may report false or malicious data to defraud rewards, resulting in service quality degradation. Moreover, the existing incentive mechanism is challenging to identify malicious workers when recruiting workers in reality, which results in low accuracy of truth discovery and waste of cost. In this paper, we propose an Incentive-based Truth Discovery (ITD) scheme to incentive credible workers to submit high-quality data, thereby enhancing the accuracy of truth discovery. In the ITD, an unmanned aerial vehicle (UAV)-assisted split-aggregation truth discovery mechanism is proposed firstly to infer the truth. The addition of the UAV can improve the accuracy of truth discovery and assist in evaluating workers’ trust. Then, we evaluate the quality of participants’ data and propose a data quality-based trust meter to update each worker’s trust to guide future recruitment efforts. Finally, a Quality-aware Trustworthy Incentive (QTI) mechanism is proposed to select credible workers for data collection and provide them with reasonable payments. The experimental results show that ITD improves the accuracy of truth discovery by 98.47%, over the state-of-the-art, at a sensing cost reduced by as high as 43.89%.