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Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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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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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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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
132
Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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相关实验视频

Updated: Jul 24, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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可变长度多目标社会阶级优化用于无线传感器网络中的信任意识数据采集.

Mohammed Ayad Saad1,2, Rosmina Jaafar1, Kalaivani Chellappan1

  • 1Department of Electrical, Electronics & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种经过修改的社会阶级多目标粒子集群优化 (SC-MOPSO) 方法,用于在无线传感器网络 (WSN) 中有效和安全地收集数据. 该方法增强了信任,提高了能源效率,减少了旅行时间,超过了现有的算法.

关键词:
收集数据收集数据收集数据这是一个多重目标的多重目标.社会阶级优化优化社会阶级优化值得信赖的 意识到 意识到长度变化的变量.

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科学领域:

  • 计算机科学 计算机科学
  • 网络工程 网络工程
  • 优化算法 优化算法

背景情况:

  • 在无线传感器网络 (WSN) 中收集数据对于物联网 (IoT) 集成至关重要.
  • 大规模的WSN部署面临效率挑战和影响数据可靠性的安全威胁.
  • 对数据源和路由节点的信任对于WSN中可靠的数据收集至关重要.

研究的目的:

  • 开发一个多目标优化方法,用于WSN收集数据.
  • 共同优化能源消耗,旅行时间,成本和对WSN数据收集的信任.
  • 提出一个修改的社会阶级多目标粒子集群优化 (SC-MOPSO) 算法.

主要方法:

  • 引入修改后的SC-MOPSO,其中包括应用依赖的类间操作员.
  • 结合解决方案生成,交会点管理和基于阶级的移动策略.
  • 从多标准决策 (MCDM) 中利用简单增量权衡 (SAW) 方法从帕雷托前线选择解决方案.

主要成果:

  • 在解决方案主导方面,SC-MOPSO和SAW表现出卓越的性能.
  • SC-MOPSO实现了一个设置覆盖范围,比NSGA-II占0.06的优势,而NSGA-II仅比SC-MOPSO占0.04的优势.
  • 与NSGA-III.III相比,提出的方法表现具有竞争力的表现.

结论:

  • 修改后的SC-MOPSO有效地解决了WSN数据收集的多目标性质.
  • 将信任作为优化目标的整合提高了WSN中的数据可靠性.
  • SC-MOPSO-SAW方法为优化复杂的WSN数据收集场景提供了强大的解决方案.