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.
Highlights
Wireless Sensor Networks (WSNs), the networks of sensor nodes, have been widely used in many promising applications such as condition monitoring, target tracking, and home security
This paper develops an error-tolerant localization method against distance outliers and anchor
This paper develops an error-tolerant localization method against distance outliers and anchor outliers
Summary
Wireless Sensor Networks (WSNs), the networks of sensor nodes, have been widely used in many promising applications such as condition monitoring, target tracking, and home security. The calculation of unknown node’s positions heavily relies on primary data, which are the distances between neighboring nodes and the position knowledge of anchors. An error-tolerant localization method is greatly needed to calculate the estimated locations of unknown nodes in the presence of undetected outliers. The uncertain value of the measured distances is obtained based on the maximum entropy theory in the lack of ranging error distribution. The Euclidean distance is calculated by the coordinates of the two anchors while the measured distance is obtained by the range-based methods. The Maximum Entropy Function (MEF) method is used to calculate the optimal estimated locations of unknown nodes by using the trustable data.
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