Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11336
Title: Improving data quality with an accumulated reputation model in participatory sensing systems
Authors: Yu, R
Liu, R
Wang, X
Cao, J 
Issue Date: 2014
Source: Sensors, Mar. 2014, v. 14, no. 3, p. 5573-5594
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.
Keywords: Contribution
Data quality
Participatory sensing
Reputation
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors 
EISSN: 1424-8220
DOI: 10.3390/s140305573
Rights: © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
The following publication Yu, R., Liu, R., Wang, X., & Cao, J. (2014). Improving data quality with an accumulated reputation model in participatory sensing systems. Sensors, 14(3), (Suppl. ), 5573-5594 is available athttps://dx.doi.org/10.3390/s140305573
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