Efficient Data Uploading for Mobile Crowdsensing via Team Collaborating and Matching
Chenghao Xu, Wei Song
Abstract
With the proliferation of mobile devices and crowdsensing applications, mobile crowdsensing (MCS) has been an appealing sensing paradigm as an alternative to the traditional sensor networks. Instead of deploying static and expensive sensors in sensing areas, MCS leverages sensors embedded in mobile devices and intelligence of mobile users to sense their surroundings, which utilizes the existing communication infrastructure. In a typical sensing cycle in MCS, recruited mobile users as workers collect data according to specified requirements and upload them to an MCS platform. A challenging problem of MCS is data uploading, which requires workers to upload their collected data in a cost-effective manner. A promising solution is to integrate edge computing and exploit the redundant resources of various edge nodes to facilitate data uploading. In this paper, we investigate such a data uploading problem in MCS, which incorporates collaborations among multiple edge nodes and properly matches a team of edge nodes with a sensing worker according to various constraints. Notably, we ensure that the demand of a worker for data uploading is fully satisfied even if served by multiple edge nodes. As we prove that the problem is NP-hard, we propose an efficient solution based on Lagrangian relaxation. Extensive numerical results show that our approach achieves a high approximation ratio and performs stably in various experiment settings.