A hybrid improved compressed particle swarm optimization WSN node location algorithm

IF 2 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Physical Communication Pub Date : 2024-09-07 DOI:10.1016/j.phycom.2024.102490

Xiaoyang Liu , Kangqi Zhang , Xiaoqin Zhang , Giacomo Fiumara , Pasquale De Meo

{"title":"A hybrid improved compressed particle swarm optimization WSN node location algorithm","authors":"Xiaoyang Liu , Kangqi Zhang , Xiaoqin Zhang , Giacomo Fiumara , Pasquale De Meo","doi":"10.1016/j.phycom.2024.102490","DOIUrl":null,"url":null,"abstract":"<div><p>The improvement of positioning accuracy in Wireless Sensor Networks (hereafter, WSN) is crucial to develop advanced Internet of Things (IOT, for short) applications. However, the conventional distance vector-hop (DV-Hop) localization algorithm has shortcomings such as low accuracy and weak stability. To overcome these shortcomings, this paper proposes a hybrid improved compressed particle swarm optimization algorithm (HICPSO), which consists of a scheme of linearly decreasing inertia weights, compressed velocity vectors, population Gaussian variants and optimal boundary selection. Then, HICPSO is integrated with DV-Hop to gradually reduce the distance error of least squares method (LSM) estimated with the efficient search advantage of HICPSO. Our simulation results show that the HICPSO algorithm possesses better computational accuracy and search performance on the 22 benchmark test functions compared with the algorithms such as the Improved Adaptive Genetic Algorithm (IAGA) and Adaptive Weighted Particle Swarm Optimizer (AWPSO). Meanwhile, compared with IAGA and AWPSO, the positioning accuracy of HICPSO-based positioning algorithm is improved by 4.28% and 4.76% respectively, and the stability is improved by one order of magnitude.</p></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"67 ","pages":"Article 102490"},"PeriodicalIF":2.0000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490724002088","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

The improvement of positioning accuracy in Wireless Sensor Networks (hereafter, WSN) is crucial to develop advanced Internet of Things (IOT, for short) applications. However, the conventional distance vector-hop (DV-Hop) localization algorithm has shortcomings such as low accuracy and weak stability. To overcome these shortcomings, this paper proposes a hybrid improved compressed particle swarm optimization algorithm (HICPSO), which consists of a scheme of linearly decreasing inertia weights, compressed velocity vectors, population Gaussian variants and optimal boundary selection. Then, HICPSO is integrated with DV-Hop to gradually reduce the distance error of least squares method (LSM) estimated with the efficient search advantage of HICPSO. Our simulation results show that the HICPSO algorithm possesses better computational accuracy and search performance on the 22 benchmark test functions compared with the algorithms such as the Improved Adaptive Genetic Algorithm (IAGA) and Adaptive Weighted Particle Swarm Optimizer (AWPSO). Meanwhile, compared with IAGA and AWPSO, the positioning accuracy of HICPSO-based positioning algorithm is improved by 4.28% and 4.76% respectively, and the stability is improved by one order of magnitude.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

一种混合改进型压缩粒子群优化 WSN 节点定位算法

提高无线传感器网络（以下简称 WSN）的定位精度对于开发先进的物联网应用至关重要。然而，传统的距离矢量跳（DV-Hop）定位算法存在精度低、稳定性差等缺点。为了克服这些缺点，本文提出了一种混合改进压缩粒子群优化算法（HICPSO），该算法由线性递减惯性权重、压缩速度矢量、种群高斯变体和优化边界选择方案组成。然后，将 HICPSO 与 DV-Hop 集成，利用 HICPSO 的高效搜索优势逐步降低最小二乘法（LSM）估计的距离误差。仿真结果表明，与改进自适应遗传算法（IAGA）和自适应加权粒子群优化器（AWPSO）等算法相比，HICPSO 算法在 22 个基准测试函数上具有更好的计算精度和搜索性能。同时，与 IAGA 和 AWPSO 相比，基于 HICPSO 的定位算法的定位精度分别提高了 4.28% 和 4.76%，稳定性提高了一个数量级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Physical Communication ENGINEERING, ELECTRICAL & ELECTRONICTELECO-TELECOMMUNICATIONS

CiteScore

5.00

自引率

9.10%

发文量

212

审稿时长

55 days

期刊介绍： PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published. Topics of interest include but are not limited to: Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.