Multi-Platform Cooperation based Incentive Mechanism in Opportunistic Mobile Crowdsensing.

Opportunistic Mobile Crowdsensing (MCS) is an attractive and cost-effective sensing paradigm because it does not affect workers' daily routines. However, its opportunistic nature in task executions can lead to low task completion rate for deadline-sensitive tasks as compared with participatory sensing. Besides, existing work in opportunistic MCS lacks of study on how to effectively coordinate among multiple service platforms for idle worker sharing so as to improve the sensing performance. In this paper, we design a multi-platform cooperation based incentive mechanism (MPCIM) for deadline-sensitive task assignment in the context of opportunistic mobile crowdsensing. The design objective is to maximize the system profit while improving the task completion rate. In MPCIM, each platform first decides how many idle workers it can provide and also how many tasks it needs assistance at different locations; Then, a cross-platform managing entity is responsible for performing maximal matching between the idle workers and excessive tasks among different platforms to improve the task completion rate while respecting individual rationality and cooperative rationality. Extensive simulation results show that MPCIM can effectively improve the system profit and also task completion rate.