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

Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

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

Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence.

Scientific reports·2026
Same author

An owl-inspired temporal transformer for enhanced shrimp detection in aquatic environments.

Scientific reports·2026
Same author

Integrating EfficientNetV2 with guided filopic diffusion for enhanced rice leaf disease recognition.

Scientific reports·2026
Same author

EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological Images.

Journal of visualized experiments : JoVE·2026
Same author

A cross-sectional study on the respiratory health status and its determinants among rural handloom workers in Tamil Nadu.

Lung India : official organ of Indian Chest Society·2026
Same author

QBrainNet: harnessing enhanced quantum intelligence for advanced brain stroke prediction from medical imaging.

Frontiers in medicine·2025

相关实验视频

Updated: Jul 10, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K

使用增强的AODV算法在飞行临时网络中安全检测恶意节点.

V Chandrasekar1, V Shanmugavalli2, T R Mahesh1

  • 1Department of Computer Science & Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, 562112, India.

Scientific reports
|April 3, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种安全的AODV算法,用于检测和隔离飞行特设网络 (FANET) 中的恶意节点. 拟议的方法通过减少数据包丢失和路由开销来提高网络安全性和性能.

关键词:
一个AODVV的AODV.飞行特设网络的飞行特设网络恶意节点恶意节点确保AODVV的安全性在TAODVV中使用.

更多相关视频

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K
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

543

相关实验视频

Last Updated: Jul 10, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K
Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.3K
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

543

科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 无线通信无线通信

背景情况:

  • 飞行特设网络 (FANET) 的使用越来越多,但由于动态节点行为和恶意攻击,它们面临着重大安全挑战.
  • 恶意节点注入虚假信息破坏FANET的稳定性,降低网络性能和可靠性.

研究的目的:

  • 提出和评估一种新的威胁检测方法,用于识别和隔离FANET中的恶意节点.
  • 通过使用安全的Ad hoc按需距离向量 (AODV) 算法来提高FANET的安全性和效率.

主要方法:

  • 实现了一个安全的AODV算法,包含基于直接/间接可靠性的信任模型,用于节点验证.
  • 开发了一个威胁检测机制,以识别和断开恶意节点的网络连接.
  • 评估性能指标,包括吞吐量,数据包丢失和路由开销与传统的AODV算法相比.

主要成果:

  • 提出的安全的AODV算法在能源消耗,吞吐量,数据包交付速度方面表现出卓越的性能,并减少了数据包丢失和路由开销.
  • 与排名第二的方法相比,实现了电力消耗降低16.5%,效率提高7.4%,数据包交付率降低9.1%.
  • 报告说,数据包丢失和路由费用减少了9.4%.

结论:

  • 开发的安全AODV算法可以有效地检测和隔离FANET中的恶意节点.
  • 拟议的方法显著改善了FANET的安全性和性能指标,与现有的算法相比.
  • 这项研究为增强无人机无线网络的稳定性和可靠性提供了强大的解决方案.