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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doaj-5f7fa3c29f864f528352265edce877f42021-03-30T00:51:13ZengIEEEIEEE Access2169-35362019-01-01715522915524110.1109/ACCESS.2019.29496128883148A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoostYi Feng0https://orcid.org/0000-0002-5299-7573Linlan Liu1Jian Shu2School of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaSchool of Information Engineering, Nanchang Hangkong University, Nanchang, ChinaSchool of Software, Nanchang Hangkong University, Nanchang, ChinaLink 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 (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks.https://ieeexplore.ieee.org/document/8883148/Wireless sensor networkslink quality predictionXGBoostimproved fuzzy C-means algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yi Feng Linlan Liu Jian Shu |
spellingShingle |
Yi Feng Linlan Liu Jian Shu A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost IEEE Access Wireless sensor networks link quality prediction XGBoost improved fuzzy C-means algorithm |
author_facet |
Yi Feng Linlan Liu Jian Shu |
author_sort |
Yi Feng |
title |
A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost |
title_short |
A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost |
title_full |
A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost |
title_fullStr |
A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost |
title_full_unstemmed |
A Link Quality Prediction Method for Wireless Sensor Networks Based on XGBoost |
title_sort |
link quality prediction method for wireless sensor networks based on xgboost |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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 (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks. |
topic |
Wireless sensor networks link quality prediction XGBoost improved fuzzy C-means algorithm |
url |
https://ieeexplore.ieee.org/document/8883148/ |
work_keys_str_mv |
AT yifeng alinkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost AT linlanliu alinkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost AT jianshu alinkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost AT yifeng linkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost AT linlanliu linkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost AT jianshu linkqualitypredictionmethodforwirelesssensornetworksbasedonxgboost |
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1724187698002919424 |