Tradeoff Between Capacity and Cost: Maximizing User Recruitment Through Collaboration in Mobile Crowdsensing

Utilizing mobile crowdsensing (MCS) for data collection and analysis has become a prominent paradigm in the Internet of Things (IoTs). However, the existing research predominantly focuses on platform-user interactions, often neglecting the potential for user collaboration, which is crucial for improving data quality and task efficiency. In practical applications, mobile users tend to cooperate with familiar individuals based on their preferences in sensing tasks. To tackle this issue, we introduce a novel MCS model that integrates user cooperation, significantly enhancing the system's overall effectiveness. Specifically, users’ capabilities and costs are synthesized and managed through a cooperation degree matrix. Additionally, cooperation is updated based on historical behaviors and user preferences. To incentivize user participation, currencies are employed for recruitment. Within this framework, we investigate the maximum collaborative user selection (MCUS) problem, which is dedicated to the problem of maximizing the amount of recruitment under user cooperation. The MCUS problem is proved to be an NP-hard problem and thus intractable. To address this, we propose the minimum weighted cost replacement (MWCR) algorithm. Experimental results demonstrate that the MWCR algorithm exhibits low complexity and high efficiency across various scales, making it an excellent solution for collaborative crowd recruitment.