A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks

作者：

Highlights：

• Proposed a deep learning-based architecture to predict the k barriers.

• An experimental study to evaluate the performance of the proposed architecture.

• Comparative results show the outperform performance concerning other benchmark algorithms.

• Presented approach can solve the problem of unnecessary computing time and cost.

摘要

•Proposed a deep learning-based architecture to predict the k barriers.•An experimental study to evaluate the performance of the proposed architecture.•Comparative results show the outperform performance concerning other benchmark algorithms.•Presented approach can solve the problem of unnecessary computing time and cost.

论文关键词：WSNs,Binary sensing model,Gaussian distribution,Uniform distribution,Barrier coverage,Deep learning

论文评审过程：Received 1 July 2021, Revised 7 August 2022, Accepted 13 August 2022, Available online 19 August 2022, Version of Record 27 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118588