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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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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...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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基于机器学习的区块链技术用于安全的V2X通信:开放的挑战和解决方案

Yonas Teweldemedhin Gebrezgiher1, Sekione Reward Jeremiah2, Xianjun Deng3

  • 1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

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概括
此摘要是机器生成的。

保护互联和自动驾驶汽车 (CAV) 需要强大的解决方案,以实现车辆到一切 (V2X) 通信. 这项调查探讨了机器学习,区块链和边缘计算,以增强V2X对网络威胁的安全性.

关键词:
在V2X安全方面,V2X的安全性是非常重要的.区块链区块链区块链区块链区块链机器学习是机器学习.多访问边缘计算边缘计算

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科学领域:

  • 网络安全 网络安全
  • 智能运输系统 智能运输系统
  • 网络通讯 网络通讯 网络通讯

背景情况:

  • 车辆与一切 (V2X) 通信对于联网和自动驾驶汽车 (CAV) 至关重要,提高了安全性和效率.
  • V2X网络面临来自对抗性攻击的重大网络安全威胁,影响CAV安全和隐私.
  • 现有的安全措施与车辆网络的动态性和实时处理需求作斗争.

研究的目的:

  • 调查和分析CAV的V2X通信当前的安全挑战.
  • 评估机器学习 (ML) 和基于区块链的解决方案在应对这些挑战中的有效性.
  • 识别漏洞和改善V2X安全的机会,特别是通过综合方法.

主要方法:

  • 关于V2X安全性的综合文献审查,重点关注ML,区块链和多访问边缘计算 (MEC).
  • 分析现有研究,将ML,区块链和MEC集成为V2X安全.
  • 开发一个概念框架,将ML,区块链和MEC协同用于增强V2X安全.

主要成果:

  • 目前用于V2X安全的ML和区块链解决方案存在重大差距,需要进一步改进.
  • 机器学习,区块链和MEC的整合显示了V2X网络中低延迟,强大和动态安全的潜力.
  • 拟议的概念框架强调了实时异常检测,分散的数据共享和可扩展性的可能性.

结论:

  • 结合ML,区块链和MEC的协同方法为确保V2X通信提供了一个有希望的方向.
  • 需要进行进一步的研究,以解决V2X安全方面的剩余挑战和不断变化的威胁.
  • 综合框架可以增强智能运输系统的安全性,可扩展性和实时功能.