TY - GEN
T1 - Minimum-cost recruitment of mobile crowdsensing in cellular networks
AU - Zhang, Fusang
AU - Jin, Beihong
AU - Liu, Hai
AU - LEUNG, Yiu Wing
AU - CHU, Xiaowen
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85015444238&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2016.7841988
DO - 10.1109/GLOCOM.2016.7841988
M3 - Conference proceeding
AN - SCOPUS:85015444238
T3 - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PB - IEEE
T2 - 59th IEEE Global Communications Conference, GLOBECOM 2016
Y2 - 4 December 2016 through 8 December 2016
ER -