Worker Recruitment Based on Edge-Cloud Collaboration in Mobile Crowdsensing System

Jinghua Zhu, Yuanjing Li, Anqi Lu, Heran Xi

Abstract: In recent years, with the rapid development of mobile Internet and smart sensor technology, mobile crowdsensing (MCS) computing model has attracted wide concern in academia, industry and business circles. MCS utilizes the sensing and computing capabilities of smart devices carried by workers to cooperate through the mobile Internet to fulfill complex tasks. Worker recruitment is a core and common research problem in MCS, which is a combinatorial optimization problem that considers tasks, workers additionally other factors to satisfy various optimization objectives and constraints. The existing methods are not suitable for large-scale and real-time sensing tasks. Thus, this paper proposes a multi-layers worker recruitment framework based on edge-cloud collaboration. At the cloud computing layer, the whole sensing area is partitioned into small grids according to task position. At the edge computing layer, real-time data processing and aggregation are performed and then a mathematical model is constructed to make decision on worker recruitment by considering a variety of factors from the perspective of workers. Experimental results on real data prove that, compared with existing methods, our method can achieve good performance in terms of spatial coverage and running time under task cost and time constraint.