A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks

作者：

Highlights：

• Proposed three methods based on GPR to predict the k barrier coverage probability.

• An experimental study to assess the performance of the proposed methods.

• Comparative results show the outperform performance concerning other variants of SVR.

• Presented methods can ameliorate the problem of unnecessary computing time and cost.

摘要

•Proposed three methods based on GPR to predict the k barrier coverage probability.•An experimental study to assess the performance of the proposed methods.•Comparative results show the outperform performance concerning other variants of SVR.•Presented methods can ameliorate the problem of unnecessary computing time and cost.

论文关键词：WSNs,GPR model,k-barrier coverage probability,Intrusion detection

论文评审过程：Received 10 October 2020, Revised 12 December 2020, Accepted 11 January 2021, Available online 20 January 2021, Version of Record 16 February 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.114603