Cooperative-Rationality-Based Multiplatform Task Assignment Mechanisms for Mobile Crowdsensing
Kun Liu, Guoliang Ji, Baoxian Zhang, Zheng Kun Yao, Cheng Li · IEEE Internet of Things Journal · 2024
Task assignment is a key issue in mobile crowdsensing (MCS). Most existing work in this area has focused on the task assignment for the single platform scenario, which can cause considerable waste of limited human resources or reduced task completion rate due to potential spatial mismatching between distributions of users and tasks. In this article, we study multiplatform cooperative task assignment. The design goal is to maximize the social welfare while ensuring cooperative and individual rationality. We formulate this problem, transform it to a maximum value flow problem, and prove its NP-hardness. We first propose a greedy-maximum-flow-based task matching (GMTA) mechanism for interplatform task matching. In GMTA, there are two phases in each time slot: 1) in the former phase, earliest-deadline-first-based intraplatform optimal task assignment is carried out at each individual platform and 2) in the second phase, greedy-maximum-flow-based task matching is carried out to perform interplatform cooperative task assignment for those overloaded tasks in the first phase. We then enhance GMTA by designing an iterative-maximum-flow-based task matching (IMTA) mechanism, which is to achieve enhanced social welfare at the cost of increased computational overhead. We deduce time complexities of both mechanisms, and prove that they satisfy cooperative and individual rationality. Extensive simulations are conducted and the simulation results demonstrate the effectiveness of our proposed mechanisms.