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Sensors 2014, 14(3), 5573-5594; doi:10.3390/s140305573

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

1
Software College, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang 100819, China
2
Department of Computing, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
3
College of Information Science and Engineering, Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang 100819, China
*
Author to whom correspondence should be addressed.
Received: 13 December 2013 / Revised: 20 January 2014 / Accepted: 10 March 2014 / Published: 20 March 2014
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [666 KB, 21 June 2014; original version 21 June 2014]   |  

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. View Full-Text
Keywords: participatory sensing; reputation; contribution; data quality participatory sensing; reputation; contribution; data quality
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Yu, R.; Liu, R.; Wang, X.; Cao, J. Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems. Sensors 2014, 14, 5573-5594.

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