Minimum-Cost Recruitment of Mobile Crowdsensing in Cellular Networks

Mobile crowdsensing (MCS) is a promising paradigm that utilizes the mobility of people and the sensing capabilities of their mobile devices to accomplish a variety of sensing tasks. In this paper, we adopt the Signaling System No.7 (SS7) as the MCS platform since SS7 can well capture trajectories and mobility patterns of the mobile users. We collect a real-world SS7 data of 1.18 million mobile users at 3512 cell towers/sites in Xiamen, China. We first analyze this dataset and reveal important characteristics of user mobility. Then, we address a Mobile User Recruitment (MUR) problem which is crucial to all MCS systems. Given SS7 data of mobile users, a set of target cells to be sensed/covered, and recruitment cost functions of the mobile users, the MUR problem is to recruit a set of mobile users such that all the target cells are covered and the total recruitment cost is minimized. Our MUR problem is general and includes the existing problems as its special cases. We prove NP-hardness of the problem. We propose an approximation algorithm to this problem and derive the approximation ratio. Extensive experiments are conducted on the real-world SS7 dataset and results show that the proposed solution outperforms two baseline algorithms by saving 22.6% and 62.9% recruitment costs, respectively, on average.