Abstract
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.
Highlights
Wireless sensor networks (WSNs) are multi-hop selforganizing networks that are formed by wireless communication, and they consist of large numbers of inexpensive micro sensor nodes that are deployed in a monitoring area [1]
We propose a method for adaptively dividing link quality grades based on SUBXBFCM algorithm in different scenarios
In this paper, we propose a link quality prediction method based on XGBOOST Extreme gradient boosting (XGBoost) for WSNs
Summary
Wireless sensor networks (WSNs) are multi-hop selforganizing networks that are formed by wireless communication, and they consist of large numbers of inexpensive micro sensor nodes that are deployed in a monitoring area [1]. The experimental results show that the improvements in the order of 20% to 30% compared with 4Bit and STLE estimators in single and multiple sender experiments, with some cases improving performance by more than 45% Some of these methods, such as [17], did not consider that the distribution characteristics of the link quality parameters are different in different scenarios. We propose a method for adaptively dividing link quality grades based on SUBXBFCM algorithm in different scenarios Others, such as those presented in [9], [10] and [11] only use hardware parameters in physical layer which ignore the calibration errors of the hardware are easy to overestimate the link quality due to ignore the packet loss. The link quality grades are determined, which reflect whether the links are good or bad
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