Link Quality Estimation Based on Extremely Fast Decision Tree

doi:10.13190/j.jbupt.2020-163

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 125-130.doi: 10.13190/j.jbupt.2020-163

Link Quality Estimation Based on Extremely Fast Decision Tree

LIU Lin-lan¹, XIAO Ting-zhong¹, XIA Yang², SHU Jian²

1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China;
2. School of Software, Nanchang Hangkong University, Nanchang 330063, China

Received:2020-09-02 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: To estimate link quality for wireless sensor networks accurately and rapidly, an approach of link quality estimation is proposed based on improved extremely fast decision tree. After analyzing the relationship between the physical parameters and the packet reception rate in different time periods, the received signal strength indicator mean, the link quality indicator mean and the signal to noise ratio mean are selected as the link quality parameters;The evaluation index is determined by the link quality levels divided by packet reception rate. A link quality estimation model is constructed based on extremely fast decision tree, and Gini index is employed as heuristic measure of decision node;the computing method of sample number, with which decision nodes look for the best attributes, is improved in terms of the height of decision node. In scenarios of indoor, corridor and parking lot, the experiment shows that compared with fuzzy logic, very fast decision tree, the earlier extremely fast decision tree etc, the proposed method has better estimation accuracy and lower time complexity.

Key words: wireless sensor networks, link quality estimation, vary fast decision tree, extremely fast decision tree

CLC Number:

TP393

LIU Lin-lan, XIAO Ting-zhong, XIA Yang, SHU Jian. Link Quality Estimation Based on Extremely Fast Decision Tree[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 125-130.

References

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Link Quality Estimation Based on Extremely Fast Decision Tree

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