AP-Assisted Online Task Assignment for Mobile Crowdsensing

Abstract

With the widespread of smart devices, mobile crowdsensing has become an attractive way to perceive and collect sensing data. In this paper, we focus on studying AP-assisted task assignment in mobile crowdsensing. The objective is to effectively reduce the average or worst-case makespan of tasks. We focus on a scenario that a task requester needs the assistance of mobile users for task accomplishment while they can meet directly or via APs in an opportunistic manner. We model the crowdsensing system and then formulate the problems under study. We then propose an AP-assisted average makespan sensitive online task assignment (AP-AOTA) algorithm and an AP-assisted largest makespan sensitive online task assignment (AP-LOTA) algorithm. In the proposed algorithms, task assignment at each step considers both the inter-encountering time between requester and each user and that between them while going through APs. We present design details of the proposed algorithms. We derive their computational complexities to be $O(mn^2)$, where $m$ is the number of tasks and $n$ is the number of users. Finally, trace-driven simulation results show that the proposed algorithms outperform existing work.