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一种用于移动无线传感器网络的节能可靠的多径传输策略。

An Energy Efficient and Reliable Multipath Transmission Strategy for Mobile Wireless Sensor Networks.

机构信息

School of Economics and Management, Wenzhou University of Technology, Wenzhou 325035, China.

School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou 325035, China.

出版信息

Comput Intell Neurosci. 2022 Aug 9;2022:8083804. doi: 10.1155/2022/8083804. eCollection 2022.

DOI:10.1155/2022/8083804
PMID:35983134
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9381252/
Abstract

Multipath data transmission is a key problem that needs to be solved urgently in wireless sensor networks. In this paper, sensor node failure, link failure, energy exhaustion, and external interference affect the stability and reliability of network data transmission. A multipath transmission strategy for wireless sensor networks based on improved shuffled frog leaping algorithm is proposed. A mathematical model of multipath transmission in wireless sensor networks is established. In the shuffled frog leaping algorithm, combined with the transition probability in the particle swarm optimization algorithm, random individuals in the subgroup are introduced to assist the search when updating the frog individual position, which improves the algorithm's ability to jump out of the local optimum and improves the quality of the optimization algorithm solution. The model is applied to multipath transmission in wireless sensor networks. Then, the shuffled frog leaping algorithm is used to update, divide, and reorganize the sensor nodes to select the optimal node to establish the optimal transmission path and improve the stability and reliability of the network. Simulation experiments show that the algorithm in this paper can ensure the reliability of data transmission, reduce the network packet loss rate and network energy consumption, and reduce the average delay of data transmission.

摘要

多径数据传输是无线传感器网络中急需解决的关键问题。本文针对传感器节点失效、链路失效、能量枯竭以及外部干扰等因素影响网络数据传输稳定性和可靠性的问题，提出了一种基于改进型蛙跳算法的无线传感器网络多径传输策略。建立了无线传感器网络多径传输的数学模型，在蛙跳算法中，结合粒子群优化算法中的迁移概率，在更新蛙个体位置时引入子群中的随机个体协助搜索，提高了算法跳出局部最优的能力，改善了优化算法解的质量。将模型应用于无线传感器网络的多径传输中，然后利用蛙跳算法进行更新、划分和重组传感器节点，选择最优节点建立最优传输路径，提高网络的稳定性和可靠性。仿真实验表明，本文算法能够保证数据传输的可靠性，降低网络丢包率和网络能耗，减少数据传输的平均时延。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/542f22722d09/CIN2022-8083804.012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/e1dfb1245b9b/CIN2022-8083804.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/23d2b120d629/CIN2022-8083804.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/1c32d5584b56/CIN2022-8083804.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/b19cd5e0f167/CIN2022-8083804.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/3001aaa52914/CIN2022-8083804.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/957786d8d0ad/CIN2022-8083804.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/9f161baa8ef3/CIN2022-8083804.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/e54bbfeb9b21/CIN2022-8083804.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/bf1e8935b9d3/CIN2022-8083804.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/7caa4dc238d1/CIN2022-8083804.010.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c289/9381252/542f22722d09/CIN2022-8083804.012.jpg

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Comput Intell Neurosci. 2022 Jun 15;2022:9004942. doi: 10.1155/2022/9004942. eCollection 2022.
2
A Data Collection Method for Mobile Wireless Sensor Networks Based on Improved Dragonfly Algorithm.基于改进型蜻蜓算法的移动无线传感器网络数据采集方法。
Comput Intell Neurosci. 2022 May 17;2022:4735687. doi: 10.1155/2022/4735687. eCollection 2022.
3
Data Collection Strategy Based on OSELM and Gray Wolf Optimization Algorithm for Wireless Sensor Networks.
基于 OSELM 和灰狼优化算法的无线传感器网络数据采集策略。
Comput Intell Neurosci. 2022 Feb 8;2022:4489436. doi: 10.1155/2022/4489436. eCollection 2022.