• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于增强阈值协议的分布式节能聚类多层聚类网络性能评估

Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.

作者信息

Bhola Jyoti, Shabaz Mohammad, Dhiman Gaurav, Vimal S, Subbulakshmi P, Soni Sunil Kumar

机构信息

Department of ECE, NIT, Hamirpur, India.

Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab India.

出版信息

Wirel Pers Commun. 2022;126(3):2175-2189. doi: 10.1007/s11277-021-08780-x. Epub 2021 Aug 21.

DOI:10.1007/s11277-021-08780-x
PMID:34456513
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8380017/
Abstract

In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.

摘要

在本研究中，通过一种新的具有增强阈值的分布式节能聚类协议（DEECET）对传感器节点进行聚类，从而建立了纯确定性系统，以构建无线传感器网络。与大多数其他现有协议相比，DEECET具有很强的动态性、高度的分布式、自配置性且能源效率很高。MATLAB仿真通过在同构和异构传感器网络的网络寿命中模拟能量耗散，提供了目标验证结果，与其他传统协议相比，使用DEECET的系统化网络在能量耗散均衡方面获得了更好的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/423c7c575560/11277_2021_8780_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/75a173784e54/11277_2021_8780_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/0c64637532e6/11277_2021_8780_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/d69e2f3cf181/11277_2021_8780_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/a6ee5c37e842/11277_2021_8780_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/bf124bc4f391/11277_2021_8780_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/f55c3e47114c/11277_2021_8780_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/76d3fa7d9f12/11277_2021_8780_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/423c7c575560/11277_2021_8780_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/75a173784e54/11277_2021_8780_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/0c64637532e6/11277_2021_8780_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/d69e2f3cf181/11277_2021_8780_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/a6ee5c37e842/11277_2021_8780_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/bf124bc4f391/11277_2021_8780_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/f55c3e47114c/11277_2021_8780_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/76d3fa7d9f12/11277_2021_8780_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d47/8380017/423c7c575560/11277_2021_8780_Fig8_HTML.jpg

相似文献

1
Performance Evaluation of Multilayer Clustering Network Using Distributed Energy Efficient Clustering with Enhanced Threshold Protocol.基于增强阈值协议的分布式节能聚类多层聚类网络性能评估
Wirel Pers Commun. 2022;126(3):2175-2189. doi: 10.1007/s11277-021-08780-x. Epub 2021 Aug 21.
2
Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN.使用K均值算法改进用于无线传感器网络的分布式能量高效聚类（DEC）协议中的簇头选择性能
Sensors (Basel). 2024 Sep 29;24(19):6303. doi: 10.3390/s24196303.
3
Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks.用于异构无线传感器网络中簇头选择的增强型鹈鹕优化算法
Sensors (Basel). 2023 Sep 6;23(18):7711. doi: 10.3390/s23187711.
4
DCE: A Distributed Energy-Efficient Clustering Protocol for Wireless Sensor Network Based on Double-Phase Cluster-Head Election.DCE：一种基于双阶段簇头选举的无线传感器网络分布式节能聚类协议。
Sensors (Basel). 2017 May 1;17(5):998. doi: 10.3390/s17050998.
5
A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment.一种适用于移动环境中异构无线传感器网络的新型能量感知分布式聚类算法。
Sensors (Basel). 2015 Dec 10;15(12):31108-24. doi: 10.3390/s151229836.
6
Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks.用于异构无线传感器网络的基于阈值数据收集的混合聚类与路由算法
Sensors (Basel). 2022 Jul 22;22(15):5471. doi: 10.3390/s22155471.
7
Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks.无线传感器网络中的节能连通覆盖方案
Sensors (Basel). 2020 Oct 28;20(21):6127. doi: 10.3390/s20216127.
8
Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer.基于改进灰狼优化器的异构无线传感器网络路由协议
Sensors (Basel). 2020 Feb 4;20(3):820. doi: 10.3390/s20030820.
9
Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.基于组合优化的无线传感器网络动态分层节能方法
Sensors (Basel). 2017 Jul 19;17(7):1665. doi: 10.3390/s17071665.
10
Energy-Efficient Clustering Mechanism of Routing Protocol for Heterogeneous Wireless Sensor Network Based on Bamboo Forest Growth Optimizer.基于竹林生长优化器的异构无线传感器网络路由协议节能聚类机制
Entropy (Basel). 2022 Jul 15;24(7):980. doi: 10.3390/e24070980.

引用本文的文献

1
Enhancing the effectiveness of wireless sensor networks through consensus estimation and universal coverage.通过共识估计和全域覆盖提高无线传感器网络的效能。
Sci Rep. 2025 Jul 10;15(1):24930. doi: 10.1038/s41598-025-10813-5.
2
ConnecSenS, a Versatile IoT Platform for Environment Monitoring: Bring Water to Cloud.ConnecSenS，一款用于环境监测的通用物联网平台：将水带到云端。
Sensors (Basel). 2023 Mar 7;23(6):2896. doi: 10.3390/s23062896.
3
Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image.基于相关性的肺癌 CT 图像分析互信息模型。
Biomed Res Int. 2022 Aug 2;2022:6451770. doi: 10.1155/2022/6451770. eCollection 2022.
4
A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.一种用于皮肤病变分割和分类的新型混合深度学习方法。
J Healthc Eng. 2022 Apr 18;2022:1709842. doi: 10.1155/2022/1709842. eCollection 2022.
5
Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks.模糊逻辑系统在健康数据管理框架性能参数上的实现。
J Healthc Eng. 2022 Apr 12;2022:9382322. doi: 10.1155/2022/9382322. eCollection 2022.
6
Homogeneous Decision Community Extraction Based on End-User Mental Behavior on Social Media.基于社交媒体中终端用户心理行为的同质决策社区抽取
Comput Intell Neurosci. 2022 Mar 8;2022:3490860. doi: 10.1155/2022/3490860. eCollection 2022.
7
Deep Learning in Healthcare System for Quality of Service.深度学习在医疗服务质量管理系统中的应用。
J Healthc Eng. 2022 Mar 8;2022:8169203. doi: 10.1155/2022/8169203. eCollection 2022.