Recruitment From Social Networks for the Cold Start Problem in Mobile Crowdsourcing
Ping Wang, Zhetao Li, Saiqin Long, Jiangtao Wang, Zhihui Tan, Haolin Liu · IEEE Internet of Things Journal · 2024
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.