title: Improving data quality with an accumulated reputation model in participatory sensing systems.
abstract: The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants' reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.
authors.affiliation: Software College, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang 100819, China. yury@mail.neu.edu.cn.
authors.forename: Ruiyun
authors.initials: R
authors.lastname: Yu
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. csrliu@comp.polyu.edu.hk.
authors.forename: Rui
authors.initials: R
authors.lastname: Liu
authors.affiliation: College of Information Science and Engineering, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang 100819, China. wangxw@mail.neu.edu.cn.
authors.forename: Xingwei
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. csjcao@comp.polyu.edu.hk.
authors.forename: Jiannong
authors.initials: J
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D012107:Research Design