A Cellular Perceptron Coverage Algorithm Based on Triangulation in WSNs

WANG Yi-jun; CHEN Zhong-ye; MIAO Rui-xin; SONG Zhong-yan

doi:10.12263/DZXB.20200864

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A Cellular Perceptron Coverage Algorithm Based on Triangulation in WSNs

PAPERS | 更新时间：2025-12-08

- A Cellular Perceptron Coverage Algorithm Based on Triangulation in WSNs
- ACTA ELECTRONICA SINICA Vol. 50, Issue 10, Pages: 2443-2451(2022)
- 作者机构：
  
  1.长春理工大学电子信息工程学院，吉林长春 130022
  2.吉林大学仪器科学与电气工程学院，吉林长春 130061
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20200864
  CLC： TN926+.2;
- Received：09 August 2020，
  
  Revised：2021-12-06，
  
  Published：25 October 2022
- 稿件说明：
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王义君,陈忠野,缪瑞新等.基于三角剖分的WSNs元胞感知覆盖算法[J].电子学报,2022,50(10):2443-2451.

WANG Yi-jun,CHEN Zhong-ye,MIAO Rui-xin,et al.A Cellular Perceptron Coverage Algorithm Based on Triangulation in WSNs[J].ACTA ELECTRONICA SINICA,2022,50(10):2443-2451.
王义君,陈忠野,缪瑞新等.基于三角剖分的WSNs元胞感知覆盖算法[J].电子学报,2022,50(10):2443-2451. DOI： 10.12263/DZXB.20200864.

WANG Yi-jun,CHEN Zhong-ye,MIAO Rui-xin,et al.A Cellular Perceptron Coverage Algorithm Based on Triangulation in WSNs[J].ACTA ELECTRONICA SINICA,2022,50(10):2443-2451. DOI： 10.12263/DZXB.20200864.

摘要

针对节点随机部署的静态无线传感器网络覆盖率低、能耗高和时延长等问题，提出基于三角剖分的无线传感器网络元胞感知覆盖算法.该算法通过改进的Delaunay三角剖分实现网络区域的唯一性划分；采用元胞思想确认节点间邻域关系，并通过设置节点感知半径完成网络拓扑控制；将节点剩余能量、时间延迟和历史转发概率作为机器学习感知器的输入数据进行训练，进而找到最优的数据转发通信路径.仿真结果表明，本文提出的算法将网络覆盖与数据传输相结合，相较于其他算法，覆盖率提高13%~34%，节点能量消耗减少了2.25~2.50 J，网络生命周期延长了25%，网络时延下降了0.25~1.18 s.

Abstract

Aiming at the problems of low coverage

high energy consumption and long delay of static Wireless Sensor Networks(WSNs) that are deployed randomly

a cellular perceptron coverage algorithm for WSNs based on triangulation is proposed. The algorithm applies improved Delaunay triangulation to achieve unique division of the network area. Then

it uses the cellular idea to confirm the neighborhood relationship between nodes and realizes network topology control by setting the node perception radius. Finally

the remaining energy of the node

the time delay and the historical forwarding probability are trained as the input data of the machine learning perceptron to find the optimal data forwarding communication path. The algorithm combines network coverage with data transmission. Compared with other algorithms

the coverage is increased by 13%~34%

the node energy consumption is reduced by 2.25~2.5 J

the network life cycle is prolonged by 25%

and the network delay is reduced by 0.25~1.18 s.

关键词

Keywords

references

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