• 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

基于接入概率和唤醒阈值的优先级调度水下无线传感器网络的性能与能耗分析

Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold.

作者信息

Li Ning, Xiang Zhiyu, Feng Liang, Gao Zhiqiang, Liu Jiaqi, Gu Haitao

机构信息

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

出版信息

Sensors (Basel). 2025 Jan 19;25(2):570. doi: 10.3390/s25020570.

DOI:10.3390/s25020570
PMID:39860940
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11768515/
Abstract

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network's varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete-time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade-off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters.

摘要

随着自主水下航行器(AUV)技术的不断进步,水下无线传感器网络(UWSN)的作用日益关键。然而,这些网络中的高能耗会显著缩短其运行寿命,而延迟问题会损害整体网络性能。为应对这些挑战,开发了一种新颖的混合数据包转发策略,该策略结合了唤醒阈值和对簇头(CH)动态调整的接入概率。这种方法旨在在保持可接受的网络延迟水平的同时节约能源。唤醒阈值限制了簇头的状态切换频率,从而降低了能耗。同时,动态接入概率根据当前网络状况调节数据包的流入,以减轻系统拥塞。此外,为了适应网络的不同传输需求,传感器节点(SN)生成的数据包根据其对延迟的敏感度分为两类。然后建立了一个具有抢占优先级的离散时间排队模型,以评估不同数据包和簇头的性能。数值结果显示了不同参数如何影响网络性能,并表明所提出的混合数据包转发机制能够有效地平衡延迟和能耗之间的关系,在特定参数范围内优于传统机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/e7e3dbd532ac/sensors-25-00570-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/b147e64f33e5/sensors-25-00570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/f7d3bc5d12c7/sensors-25-00570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/e30077f6f167/sensors-25-00570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/29f68158eab2/sensors-25-00570-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/3e49799394fe/sensors-25-00570-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/9a678b99b48e/sensors-25-00570-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/1e76e205d7a2/sensors-25-00570-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/34d8605c7f5a/sensors-25-00570-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/786b500e7f17/sensors-25-00570-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/deb2f2bb90f5/sensors-25-00570-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/efdbde5bd3da/sensors-25-00570-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/c9988e9b1240/sensors-25-00570-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/d17a8f15bb84/sensors-25-00570-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/e7e3dbd532ac/sensors-25-00570-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/b147e64f33e5/sensors-25-00570-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/f7d3bc5d12c7/sensors-25-00570-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/e30077f6f167/sensors-25-00570-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/29f68158eab2/sensors-25-00570-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/3e49799394fe/sensors-25-00570-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/9a678b99b48e/sensors-25-00570-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/1e76e205d7a2/sensors-25-00570-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/34d8605c7f5a/sensors-25-00570-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/786b500e7f17/sensors-25-00570-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/deb2f2bb90f5/sensors-25-00570-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/efdbde5bd3da/sensors-25-00570-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/c9988e9b1240/sensors-25-00570-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/d17a8f15bb84/sensors-25-00570-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/62b3/11768515/e7e3dbd532ac/sensors-25-00570-g014.jpg

相似文献

1
Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold.基于接入概率和唤醒阈值的优先级调度水下无线传感器网络的性能与能耗分析
Sensors (Basel). 2025 Jan 19;25(2):570. doi: 10.3390/s25020570.
2
A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs.一种基于协同设计的可靠低延迟且节能的水下无线传感器网络传输协议
Sensors (Basel). 2020 Nov 8;20(21):6370. doi: 10.3390/s20216370.
3
High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks.水下自主航行器辅助的水下传感器网络中的高效聚类路由协议
Sensors (Basel). 2024 Oct 16;24(20):6661. doi: 10.3390/s24206661.
4
Energy-Efficient Depth-Based Opportunistic Routing with Q-Learning for Underwater Wireless Sensor Networks.基于Q学习的水下无线传感器网络节能深度机会路由
Sensors (Basel). 2020 Feb 14;20(4):1025. doi: 10.3390/s20041025.
5
An efficient and reliable geographic routing protocol based on partial network coding for underwater sensor networks.一种基于部分网络编码的高效可靠的水下传感器网络地理路由协议。
Sensors (Basel). 2015 May 28;15(6):12720-35. doi: 10.3390/s150612720.
6
An Efficient Routing Protocol Based on Stretched Holding Time Difference for Underwater Wireless Sensor Networks.基于拉伸保持时间差的水下无线传感器网络高效路由协议。
Sensors (Basel). 2019 Dec 16;19(24):5557. doi: 10.3390/s19245557.
7
Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks.基于模糊决策的水下传感器网络节能数据包转发方案
Sensors (Basel). 2021 Jun 25;21(13):4368. doi: 10.3390/s21134368.
8
Traffic Priority Based Channel Assignment Technique for Critical Data Transmission in Wireless Body Area Network.基于流量优先级的无线体域网关键数据传输信道分配技术。
J Med Syst. 2018 Sep 20;42(11):206. doi: 10.1007/s10916-018-1054-y.
9
Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks.用于水下无线传感器网络的空洞规避机会路由协议
Sensors (Basel). 2021 Mar 10;21(6):1942. doi: 10.3390/s21061942.
10
Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility.基于强化学习的水下无线传感器网络中具有被动移动性的数据转发。
Sensors (Basel). 2019 Jan 10;19(2):256. doi: 10.3390/s19020256.

本文引用的文献

1
An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks.一种基于元启发式算法的水下无线传感器网络聚类与路由协议
Sensors (Basel). 2022 Jan 6;22(2):415. doi: 10.3390/s22020415.
2
Low-Power Wireless Sensor Network Using Fine-Grain Control of Sensor Module Power Mode.基于传感器模块功率模式细粒度控制的低功耗无线传感器网络。
Sensors (Basel). 2021 May 4;21(9):3198. doi: 10.3390/s21093198.
3
A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks.一种适用于无线传感器网络的自适应睡眠/唤醒调度方法。
IEEE Trans Cybern. 2018 Mar;48(3):979-992. doi: 10.1109/TCYB.2017.2669996. Epub 2017 Mar 3.