A Task Bundling based Multi-Platform Cooperation Mechanism for Mobile Crowdsensing
Zixing Zhao, Baoxian Zhang, Chen Liu, Yao Zheng, Cheng Li · 2024
Mobile crowdsensing (MCS) is a cost-effective sensing paradigm by incentivizing mobile users to perform sensing tasks using their smartphones with rich embedded sensors. An important problem in MCS is how to achieve high task completion rate for location dependent tasks since some of them can be far away from potential users. Most existing work in this aspect assumes that there is only one service platform without consideration of existence of multiple platforms and also impact of their cooperation on task completion rate. In this paper, we design a task bundling based multi-platform cooperation mechanism (TBMCM) for completion of location dependent sensing tasks. The design objective is to maximize the system profit while improving the task completion rate. TBMCM works in a slot-by-slot manner. In each slot, each platform first assigns its tasks and task bundles to its registered users through reverse auctions and then submits information about its idle users and not-assigned-yet unpopular tasks to a cross-platform cooperation managing entity (CCM) for cross-platform cooperation. Then, the CCM entity releases the tasks and task bundles created using its collected tasks to the idle users. Meanwhile, each platform has the option to bundle its own tasks with the tasks provided by the CCM entity. A task bundling method is then designed to perform effective bundling between unpopular and popular tasks for improved task completion rate. We show the proposed mechanism satisfies individual and cooperative rationality. Extensive simulation results show the high performance of TBMCM in terms of task completion rate and system profit.