Weight convergence analysis of DV-hop localization algorithm with GA Weight convergence analysis of DV-hop localization algorithm with GA
The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the small...
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| Vydané v: | Soft computing (Berlin, Germany) Ročník 24; číslo 23; s. 18249 - 18258 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2020
Springer Nature B.V |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4
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| AbstractList | The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R. The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R.The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R. The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4 R . The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4 . |
| Author | Cui, Zhihua Chen, Jinjun Cai, Xingjuan Wang, Penghong Zhang, Wensheng |
| Author_xml | – sequence: 1 givenname: Xingjuan surname: Cai fullname: Cai, Xingjuan organization: School of Computer Science and Technology, Taiyuan University of Science and Technology – sequence: 2 givenname: Penghong orcidid: 0000-0002-5401-5676 surname: Wang fullname: Wang, Penghong email: penghongwang@sina.cn organization: School of Computer Science and Technology, Taiyuan University of Science and Technology – sequence: 3 givenname: Zhihua surname: Cui fullname: Cui, Zhihua email: zhihua.cui@hotmail.com organization: School of Computer Science and Technology, Taiyuan University of Science and Technology – sequence: 4 givenname: Wensheng surname: Zhang fullname: Zhang, Wensheng organization: State Key Laboratory of Intelligent Control and Management of Complex System, Institute of Automation, Chinese Academy of Sciences – sequence: 5 givenname: Jinjun surname: Chen fullname: Chen, Jinjun organization: School of Computer Science and Technology, Taiyuan University of Science and Technology, Department of Computer Science and Software Engineering, Swinburne University of Technology |
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| Cites_doi | 10.1016/j.adhoc.2014.07.025 10.1016/S1473-3099(20)30086-4 10.1016/j.future.2013.01.010 10.1016/j.compeleceng.2017.12.036 10.1007/s10732-014-9257-y 10.1016/j.jpdc.2016.10.011 10.1007/s00366-011-0241-y 10.1002/cpe.5464 10.1155/2015/187670 10.1016/S0140-6736(20)30566-3 10.1109/JSAC.2014.2328098 10.1109/MCOM.2014.6736746 10.1109/JSEN.2019.2927733 10.1109/KAMW.2008.4810605 10.1109/COMST.2015.2444095 10.1023/A:1023403323460 10.1155/2014/436891 10.1002/dac.4431 10.1007/s11235-016-0196-9 10.1007/s10916-010-9449-4 10.1007/s11277-018-6084-8 10.1109/EmbeddedCom-ScalCom.2009.55 |
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| Keywords | Genetic algorithm (GA) Positional precision Convergence analyses Mathematical weight model DV-hop |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Control Convergence Engineering Genetic algorithms Localization Mathematical analysis Mathematical Logic and Foundations Mathematical models Mechatronics Methodologies and Application Nodes Optimization algorithms Robotics Sensors Wireless communications |
| Subtitle | Weight convergence analysis of DV-hop localization algorithm with GA |
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| Title | Weight convergence analysis of DV-hop localization algorithm with GA |
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