A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost

Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SU...

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Veröffentlicht in:	IEEE access Jg. 7; S. 155229 - 155241
Hauptverfasser:	Feng, Yi, Liu, Linlan, Shu, Jian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Clustering Communication Correlation analysis Correlation coefficients Estimation Hardware improved fuzzy C-means algorithm link quality prediction Physical layer Prediction algorithms Prediction models Predictive models Regression analysis Sensors Signal quality Signal strength Signal to noise ratio Support vector machines Wireless communications Wireless networks Wireless sensor networks XGBoost
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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