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1

School of Information and Electrical Engineering, Harbin Institute of Technology at WeiHai, No.2 WenHua west road, Weihai 264209, China

2

Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, No.1, Jin Ji road, Guilin 541004, China

3

Automatic Test and Control Institute, Harbin Institute of Technology, Harbin 150080, China

4

Multimedia Communications Research Laboratory MCRLab, University of Ottawa, Ottawa, ON K1N 6N5, Canada





*

Author to whom correspondence should be addressed.



Abstract For communication distance estimations in Wireless Sensor Networks WSNs, the RSSI Received Signal Strength Indicator value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval data clustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches. View Full-Text

Keywords: wireless sensor network; distance estimation; RSSI; uncertain data; data clustering algorithm wireless sensor network; distance estimation; RSSI; uncertain data; data clustering algorithm





Autor: Qinghua Luo 1,2,* , Yu Peng 3, Xiyuan Peng 3 and Abdulmotaleb El Saddik 4

Fuente: http://mdpi.com/



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