Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing

TL;DR

This paper introduces social-network-based algorithms for worker recruitment in Mobile Crowd Sensing, leveraging social influence to improve coverage and efficiency over traditional methods.

Contribution

It proposes two novel algorithms, Basic-Selector and Fast-Selector, for seed selection in social influence-based worker recruitment in MCS.

Findings

Fast-Selector outperforms baseline methods in coverage.

Fast-Selector is more efficient than Basic-Selector.

Empirical results on real datasets validate the effectiveness.

Abstract

Worker recruitment is a crucial research problem in Mobile Crowd Sensing (MCS). While previous studies rely on a specified platform with a pre-assumed large user pool, this paper leverages the influenced propagation on the social network to assist the MCS worker recruitment. We first select a subset of users on the social network as initial seeds and push MCS tasks to them. Then, influenced users who accept tasks are recruited as workers, and the ultimate goal is to maximize the coverage. Specifically, to select a near-optimal set of seeds, we propose two algorithms, named Basic-Selector and Fast-Selector, respectively. Basic-Selector adopts an iterative greedy process based on the predicted mobility, which has good performance but suffers from inefficiency concerns. To accelerate the selection, Fast-Selector is proposed, which is based on the interdependency of geographical positions among friends. Empirical studies on two real-world datasets verify that Fast-Selector achieves higher coverage than baseline methods under various settings, meanwhile, it is much more efficient than Basic-Selector while only sacrificing a slight fraction of the coverage.