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Sensors 2016, 16(7), 1041; doi:10.3390/s16071041

An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks

School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Xueyuan Road No.37, Haidian District, Beijing 100191, China
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Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 31 March 2016 / Revised: 19 June 2016 / Accepted: 26 June 2016 / Published: 7 July 2016
(This article belongs to the Section Sensor Networks)
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Abstract

Precise localization has attracted considerable interest in Wireless Sensor Networks (WSNs) localization systems. Due to the internal or external disturbance, the existence of the outliers, including both the distance outliers and the anchor outliers, severely decreases the localization accuracy. In order to eliminate both kinds of outliers simultaneously, an outlier detection method is proposed based on the maximum entropy principle and fuzzy set theory. Since not all the outliers can be detected in the detection process, the Maximum Entropy Function (MEF) method is utilized to tolerate the errors and calculate the optimal estimated locations of unknown nodes. Simulation results demonstrate that the proposed localization method remains stable while the outliers vary. Moreover, the localization accuracy is highly improved by wisely rejecting outliers. View Full-Text
Keywords: wireless sensor networks; localization; outliers; maximum entropy principle; fuzzy set theory wireless sensor networks; localization; outliers; maximum entropy principle; fuzzy set theory
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. (CC BY 4.0).

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MDPI and ACS Style

Wang, D.; Wan, J.; Wang, M.; Zhang, Q. An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks. Sensors 2016, 16, 1041.

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