一个增强的花花授粉算法与高斯扰动为WSN的节点位置
Jun Zheng1, Ting Yuan2, Wenwu Xie2
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种增强的花花授粉算法 (FPA) 与高斯扰动,以提高无线传感器网络 (WSN) 的定位精度. 与现有算法相比,新方法EFPA-G实现了优越的定位和稳定性.
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:
- 准确的本地化对于物联网 (IoT) 和无线传感器网络 (WSN) 应用至关重要.
- 传统的无范围本地化算法往往缺乏所需的精度.
- 现有的算法在平衡全球勘探和本地开发方面面临挑战,导致过早的融合.
研究的目的:
- 提出一个增强的本地化算法,EFPA-G,将改进的花粉算法 (FPA) 与高斯扰动和DV-Hop方法相结合.
- 通过加强其全球勘探和当地开采能力,解决FPA中过早的融合问题.
- 为了提高WSNs的本地化准确性和稳定性.
主要方法:
- 开发了一种增强的花花授粉算法 (EFPA-G),结合高斯扰动来提高全球和本地搜索能力.
- 整合了最佳的个人和莱维飞行策略,以增强种群变异性.
- 利用DV-Hop方法在WSN模拟中进行本地化.
- 使用26个基准函数和WSN模拟来评估性能.
主要成果:
- 与基准函数上的最先进的算法相比,EFPA-G算法展示了优越的融合和搜索能力.
- 在WSN模拟中,EFPA-G实现了19.5182%的正常化平均平方误差,超过了RACS算法 (20.2650%).
- EFPA-G实现了4.88E+00的平均距离误差,也优于RACS算法 (5.07E+00).
结论:
- 拟议的EFPA-G算法显著提高了WSN的本地化准确性和稳定性.
- 高斯扰动和莱维飞行策略的整合有效地解决了FPA的过早融合.
- 在物联网和WSN应用中,EFPA-G代表了高精度本地化的一个有希望的进步.
更多相关视频
07:23Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
Published on: August 4, 2014
23.1K
08:13SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
Published on: December 25, 2017
8.2K
相关概念视频
Pollination and Flower Structure
65.0K
Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.
65.0K
Errors in Global Positioning System
68
Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
68
Frequency-dependent Selection
22.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.1K
Field Application of Global Positioning System
68
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
68
