A Multiplatform-Cooperation-Based Task Assignment Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) has been an effective sensing paradigm by utilizing the smart devices carried by mobile users to complete sensing tasks at different locations. An important problem in MCS is how to achieve effective task assignment in the context of opportunistic sensing, where mobile users are selectively recruited to perform tasks in an opportunistic way. However, most existing work in this aspect suppose there are only one service platform and further the sensing qualities of users are known a priori. In this article, we study the task assignment when there are multiple service platforms and further the sensing qualities of users are unknown a priori. The design objective is to maximize the overall sensing qualities of finished tasks at all platforms. For this purpose, we build a multiplatform cooperation framework and formulate the task quality maximization problem in this case as a 0–1 integer linear programming (ILP) problem. We propose a multiplatform-cooperation-based task assignment mechanism (MCTA). MCTA includes two phases. The first phase establishes stable cooperation relationship among platforms while respecting their respective cooperation willingness, and for this phase, we propose a cross-platform cooperation relationship construction algorithm. The second phase performs effective online task assignment, and for this phase, we propose two online multiarmed bandit (MAB) with sleeping -arms-based user selection algorithms using local and global learning, respectively, based on whether cross-platform user-sensing-quality learning is allowed. We derive the regrets of the proposed algorithms and prove that MCTA has the properties of cooperation stability and computation efficiency. Extensive simulation results show the high performance of our proposed MCTA mechanism as compared with the existing work.