Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Inclusive Fitness00:57

Inclusive Fitness

23.7K
Most altruistic behavior—in which one animal helps another at a cost to themselves—occurs between relatives. Scientists think these altruistic behaviors evolved because they increase the inclusive fitness of the animal providing help.
23.7K
Cluster Sampling Method01:20

Cluster Sampling Method

11.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Optimized energy efficient clustering in WSNs through modified zebra optimization.

Scientific reports·2025
Same author

An energy efficient hierarchical routing approach for UWSNs using biology inspired intelligent optimization.

Scientific reports·2025
Same author

An adaptive coverage method for dynamic wireless sensor network deployment using deep reinforcement learning.

Scientific reports·2025
Same author

An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm.

Scientific reports·2025
Same author

GSHFA-HCP: a novel intelligent high-performance clustering protocol for agricultural IoT in fragrant pear production monitoring.

Scientific reports·2024
Same author

[Advances in sleep-related hypermotor epilepsy].

Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences·2020

相关实验视频

Updated: May 7, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K

基于高斯基突变适应性人工鱼群算法的IWSN改进的节能集群方法.

Yeshen Lan1, Chuchu Rao2, Qike Cao3

  • 1School of Mechanical and Electrical Engineering, Quzhou College of Technology, 324000, Quzhou, China.

Scientific reports
|November 7, 2024
PubMed
概括

本研究介绍了工业无线传感器网络 (IWSNs) 的新集群模型和路由协议,以提高能源效率. 高斯基突变自适应人工鱼群算法 (GAAFSA) 显著提高了网络寿命和数据传输可靠性.

更多相关视频

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.5K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

487

相关实验视频

Last Updated: May 7, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.9K
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.5K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

487

科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 网络工程 网络工程

背景情况:

  • 工业无线传感器网络 (IWSNs) 面临的挑战是当前集群路由方法的能源效率低下.
  • 有效的基于集群的路由协议对于优化IWSN性能和延长网络寿命至关重要.

研究的目的:

  • 设计一种新的集群模型,以便在IWSNs中高效地选择集群头 (CH) 和传输数据.
  • 提出一种新的集群路由协议,高斯变异自适应人工鱼群算法 (GAAFSA),以解决能源效率低下的问题.

主要方法:

  • 开发了一个集群模型,考虑了CH能量,基站 (BS) 距离,数据包丢失率和数据延迟.
  • 在人工鱼群算法 (AFSA) 中引入了高斯基基突变策略和自适应策略,以防止局部最佳和过早的融合.
  • 在实验中,将GAAFSA协议与五个现有的方案 (CMSTR,D2CRP,EEHCHR,ESCVAD,BAFSA) 进行了比较.

主要成果:

  • 与其他方案相比,GAAFSA协议在网络能耗,系统寿命,数据传输可靠性和延迟方面表现优越.
  • 实现了至少15.68%的网络寿命改善.
  • 基站 (BS) 接收的数据包增加了至少7.46%,数据包丢失率降低了至少15.28%.

结论:

  • 拟议的GAAFSA协议有效地优化了IWSN性能,并延长了网络寿命.
  • 显著减少了网络内的能量损失.
  • 显著提高整体网络服务质量 (QoS).