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Sensors 2016, 16(6), 788; doi:10.3390/s16060788

Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy Control

College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 5 March 2016 / Revised: 17 May 2016 / Accepted: 25 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)

Abstract

Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to determine a target on a map with unknown transmission information. To increase the accuracy of localization, we propose a confidence value for each anchor node to indicate its credibility for participating in the estimation. An automatic calibration device is designed to help acquire the values. The acceleration sensor and unscented Kalman filter (UKF) are also introduced to reduce the influence of measuring noise in the application. Energy control is another key point in WSN systems and may prolong the lifetime of the system. Thus, a quadtree structure is constructed to describe the region correlation between neighboring areas, and the unnecessary anchor nodes can be detected and set to sleep to save energy. The localization system is implemented on real-time Texas Instruments CC2430 and CC2431 embedded platforms, and the experimental results indicate that these mechanisms achieve a high accuracy and low energy cost. View Full-Text
Keywords: wireless sensor network; indoor localization; back propagation; unscented Kalman filter; energy control wireless sensor network; indoor localization; back propagation; unscented Kalman filter; energy control
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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

Zou, T.; Lin, S.; Li, S. Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy Control. Sensors 2016, 16, 788.

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