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Mathematical Problems in Engineering
Volume 2014, Article ID 898761, 10 pages
Research Article

Optimized Reputable Sensing Participants Extraction for Participatory Sensor Networks

1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2College of Science, Yanbian University, Yanji 133002, China

Received 1 April 2014; Accepted 28 July 2014; Published 29 September 2014

Academic Editor: Lu Zhen

Copyright © 2014 Weiwei Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


By collecting data via sensors embedded personal smart devices, sensing participants play a key role in participatory sensor networks. Using information provided by reputable sensing participants ensures the reliability of participatory sensing data. Setting a threshold for the reputation, and those whose reputations are bigger than this value are regarded as reputable. The bigger the threshold value is, the more reliable the extracted reputable sensing participant is. However, if the threshold value is too big, only very limited participatory sensing data can be involved. This may cause unexpected bias in information collection. Existing works did not consider the relationship between the reliability of extracted reputable sensing participants and the ratio of usable participatory sensing data. In this work, we propose a criterion for optimized reputable sensing participant extraction in participatory sensor networks. This is achieved based on the mathematical analysis on the ratio of available participatory sensing data and the reliability of extracted reputable sensing participants. Our suggested threshold value for reputable sensing participant extraction is only related to the power of sensing participant’s reputation distribution. It is easy to be applied in real applications. Simulation results tested on real application data further verified the effectiveness of our proposed method.