Dynamic User Recruitment in Edge-Aided Mobile Crowdsensing

Abstract

Most of the existing user recruitment methods are designed for the two-tier structure. However, the computational consumption of the cloud platform rises rapidly as data volumes increase. In recent years, edge computing has been introduced into mobile crowdsensing. In this paper, we study dynamic user recruitment with edge-aided mobile crowdsensing. Specifically, we consider the scenarios for different sensing tasks. One is the long-duration task, and the other is the short-duration task. For the former, it is time-insensitive, while the latter is time-sensitive. In the scenario with the long-duration task, we consider a simple offline scenario and propose an Edge-Node User Recruitment (ENUR) algorithm to recruit users statically. Then, we consider the online scenario and propose a Budget Re-Distribution algorithm for Edge Nodes User Recruitment (BRD-ENUR). It dynamically redistributes the budget according to the task completion ratio of different edge nodes. In the scenario with the short-duration task, the whole sensing process consists of multiple rounds. In each round, edge nodes need to recruit users between selected users (exploitation) and unselected users (exploration) under the budget. We model such a user recruitment process as a novel combinatorial multi-armed bandit problem. Then, we propose an Edge Nodes User Recruitment algorithm based on combinatorial Multi-armed Bandit (EN-URMB). Extensive experiments show the validity and reliability of our user recruitment algorithms based on the real data set.