Recruiting MCS Workers Strategy with Non-Fixed Reward in Social Network

Abstract

In Mobile crowdsensing (MCS), the platform needs an adequate user group to accomplish tasks. Its recruiting worker strategy is essential for sensor data quality. Social-network-assisted worker recruitment effectively expands task coverage. However existing studies enclose two impractical assumptions: influence between users is determined by the number of friends and the recruiting reward is fixed. To solve this problem, a novel influence and cost trade-off (ICT) algorithm is proposed to apply the worker recruitment strategy in the real world. ICT uses linear equations to estimate influence and cost iteratively under the impact of the seed set. Using the influence model based on social interaction, the algorithm selects a near-optimal set of seeds by the revenue-cost-ratio. Empirical studies on three realworld datasets verify that ICT achieves higher performance than baseline methods under various settings.