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Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
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...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

670
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...
670
Short-distance Transport of Resources02:12

Short-distance Transport of Resources

16.1K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
555
Parallel Processing01:20

Parallel Processing

181
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Optimal Foraging00:48

Optimal Foraging

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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相关实验视频

Updated: Jul 19, 2025

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

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SDN-IoT:基于SDN的物联网高效集群方案,使用改进的Sailfish优化算法.

Ramin Mohammadi1, Sedat Akleylek2,3,4, Ali Ghaffari5,6

  • 1Ondokuz Mayis University, Department of Computational Sciences, Samsun, Türkiye.

PeerJ. Computer science
|August 7, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了使用软件定义网络 (SDN) 和改进的Sailfish优化 (ISFO) 算法对物联网 (IoT) 的高效集群方法,显著降低物联网网络的能源消耗.

关键词:
集群集成是指集群集成.在SDN中,SDN是SDN.帆鱼优化算法 帆鱼优化算法

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科学领域:

  • 计算机科学 计算机科学
  • 网络化 网络化 网络化
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 在数据传输方面面临挑战,原因是资源限制和数十亿设备的异质性.
  • 基于集群的数据传输和软件定义网络 (SDN) 为物联网的可扩展性,网络寿命和灵活性提供了潜在的解决方案.

研究的目的:

  • 为物联网网络提出一个高效的,基于SDN的集群方案.
  • 通过使用优化算法来提高物联网设备数据传输可靠性和能源效率.

主要方法:

  • 开发了一个基于SDN的集群方案,集成了改进的帆船鱼优化 (ISFO) 算法.
  • 在SDN控制器上实现ISFO模型,以管理物联网设备的集群头 (CH) 节点.
  • 通过对150个和300个物联网节点进行模拟来评估性能.

主要成果:

  • 与LEACH和LEACH-E相比,ISFO模型显示了显著的能源消耗减少.
  • 对于150个节点,节能约为21.42% (与LEACH相比) 和17.28% (与LEACH-E相比).
  • 对于300个节点,节能达到大约37.84% (与LEACH相比) 和27.23% (与LEACH-E相比).

结论:

  • 拟议的基于SDN的ISFO集群计划有效地提高了物联网网络的能源效率.
  • 这种方法为管理异质物联网设备及其数据传输提供了可扩展和强大的解决方案.
  • 与现有的协议相比,ISFO算法为物联网集群提供了优越的优化方法.