Analysis of Improved Particle Swarm Algorithm in Wireless Sensor Network Localization

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2023-09-11 DOI:10.4108/ew.3431

Yafeng Chen

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引用次数: 0

Abstract

WSN localization occupies an important position in the practical application of WSN. To complete WSN localization efficiently and accurately, the article constructs the objective function based on the target node location constraints and maximum likelihood function. It avoids premature convergence through the PSO algorithm based on chaos search and backward learning. Based on linear fitting, the node-flipping fuzzy detection method is proposed to perform the judgment of node flipping fuzzy phenomenon. And the detection method is combined with the localization algorithm, and the final WSN localization algorithm is obtained after multi-threshold processing. After analysis, it is found that compared with other PSO algorithms, the MTLFPSO algorithm used in the paper has better performance with the highest accuracy of 83.1%. Different threshold values will affect the favorable and error detection rates of different WSNs. For type 1 WSNs, the positive detection rate of the 3-node network is the highest under the same threshold value, followed by the 4-node network; when the threshold value is 7.5 (3 ), the positive detection rate of the 3-node network is 97.8%. Different numbers of anchor nodes and communication radius will have specific effects on the number of definable nodes and relative localization error, in which the lowest relative localization error of the MTLFPSO algorithm is 3.4% under different numbers of anchor nodes; the lowest relative localization error of MTLFPSO algorithm is 2.5% under different communication radius. The article adopts the method to achieve accurate and efficient localization of WSNs.

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改进粒子群算法在无线传感器网络定位中的应用分析

无线传感器网络定位在无线传感器网络的实际应用中占有重要地位。为了高效、准确地完成WSN定位，本文基于目标节点位置约束和极大似然函数构造了目标函数。通过基于混沌搜索和逆向学习的粒子群算法避免了早熟收敛。提出了基于线性拟合的节点翻转模糊检测方法，对节点翻转模糊现象进行判断。并将检测方法与定位算法相结合，经过多阈值处理得到最终的WSN定位算法。经过分析发现，与其他粒子群算法相比，本文使用的MTLFPSO算法性能更好，准确率最高达到83.1%。不同的阈值会影响不同wsn的有利检出率和错误检出率。对于1型wsn，在相同阈值下，3节点网络的阳性检出率最高，4节点网络次之;当阈值为7.5(3)时，3节点网络的阳性检出率为97.8%。不同锚节点数量和通信半径会对可定义节点数量和相对定位误差产生特定影响，其中MTLFPSO算法在不同锚节点数量下的相对定位误差最低为3.4%;在不同通信半径下，MTLFPSO算法的相对定位误差最小为2.5%。本文采用该方法实现了无线传感器网络的准确、高效定位。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.