Zobrazit v EDS

A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization.

Uloženo v:
Podrobná bibliografie
Název: A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization.
Autoři: Wang, Shuxin1 (AUTHOR), Zhang, Qingchen2 (AUTHOR), Zheng, Yejun3 (AUTHOR), Yue, Yinggao1,2 (AUTHOR), Cao, Li2 (AUTHOR), Xiong, Mengji1,3 (AUTHOR)
Zdroj: Biomimetics (2313-7673). Nov2025, Vol. 10 Issue 11, p750. 33p.
Témata: *WIRELESS sensor networks, *SWARM intelligence, *RESOURCE allocation, *MATHEMATICAL optimization, *CONSTRAINED optimization, *OPTIMIZATION algorithms
Abstrakt: WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to 'coverage holes' in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=search&db=pmc&term=2313-7673[TA]+AND+750[PG]+AND+2025[PDAT]
    Name: FREE - PubMed Central (ISSN based link)
    Category: fullText
    Text: Full Text
    Icon: https://imageserver.ebscohost.com/NetImages/iconPdf.gif
    MouseOverText: Check this PubMed for the article full text.
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:asx&genre=article&issn=23137673&ISBN=&volume=10&issue=11&date=20251101&spage=750&pages=750-782&title=Biomimetics (2313-7673)&atitle=A%20Coverage%20Optimization%20Approach%20for%20Wireless%20Sensor%20Networks%20Using%20Swarm%20Intelligence%20Optimization.&aulast=Wang%2C%20Shuxin&id=DOI:10.3390/biomimetics10110750
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Wang%20S
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: asx
DbLabel: Academic Search Index
An: 189652749
RelevancyScore: 1452
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1452.14831542969
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Shuxin%22">Wang, Shuxin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Qingchen%22">Zhang, Qingchen</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Yejun%22">Zheng, Yejun</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yue%2C+Yinggao%22">Yue, Yinggao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Li%22">Cao, Li</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xiong%2C+Mengji%22">Xiong, Mengji</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Biomimetics+%282313-7673%29%22">Biomimetics (2313-7673)</searchLink>. Nov2025, Vol. 10 Issue 11, p750. 33p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22WIRELESS+sensor+networks%22">WIRELESS sensor networks</searchLink><br />*<searchLink fieldCode="DE" term="%22SWARM+intelligence%22">SWARM intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22RESOURCE+allocation%22">RESOURCE allocation</searchLink><br />*<searchLink fieldCode="DE" term="%22MATHEMATICAL+optimization%22">MATHEMATICAL optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22CONSTRAINED+optimization%22">CONSTRAINED optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22OPTIMIZATION+algorithms%22">OPTIMIZATION algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: WSN coverage optimization faces two key challenges: firstly, traditional algorithms are prone to getting stuck in local optima, leading to 'coverage holes' in node deployment; Secondly, in dynamic scenarios (such as imbalanced energy consumption of nodes), the convergence speed of the algorithm is slow, making it difficult to maintain high coverage in real time. This study focuses on the coverage optimization problem of wireless sensor networks (WSNs) and proposes improvements to the Flamingo Search Optimization Algorithm (FSA). Specifically, the algorithm is enhanced by integrating the elite opposition-based learning strategy and the stagewise step-size control strategy, which significantly improves its overall performance. Additionally, the introduction of a cosine variation factor combined with the stagewise step-size control strategy enables the algorithm to effectively break free from local optima constraints in the later stages of iteration. The improved Flamingo Algorithm is applied to optimize the deployment strategy of sensing nodes, thereby enhancing the coverage rate of the sensor network. First, an appropriate number of sensing nodes is selected according to the target area, and the population is initialized using a chaotic sequence. Subsequently, the improved Flamingo Algorithm is adopted to optimize and solve the coverage model, with the coverage rate as the fitness function and the coordinates of all randomly distributed sensing nodes as the initial foraging positions. Next, a search for candidate foraging sources is performed to obtain the coordinates of sensing nodes with higher fitness; the coordinate components of these candidate foraging sources are further optimized through chaos theory to derive the foraging source with the highest fitness. Finally, the coordinates of the optimal foraging source are output, which correspond to the coordinate values of all sensing nodes in the target area. Experimental results show that after 100 and 200 iterations, the coverage rate of the improved Flamingo Search Optimization Algorithm is 7.48% and 5.68% higher than that of the original FSA, respectively. Furthermore, the findings indicate that, by properly configuring the Flamingo population size and the number of iterations, the improved algorithm achieves a higher coverage rate compared to other benchmark algorithms. [ABSTRACT FROM AUTHOR]
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=189652749
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/biomimetics10110750
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 33
        StartPage: 750
    Subjects:
      – SubjectFull: WIRELESS sensor networks
        Type: general
      – SubjectFull: SWARM intelligence
        Type: general
      – SubjectFull: RESOURCE allocation
        Type: general
      – SubjectFull: MATHEMATICAL optimization
        Type: general
      – SubjectFull: CONSTRAINED optimization
        Type: general
      – SubjectFull: OPTIMIZATION algorithms
        Type: general
    Titles:
      – TitleFull: A Coverage Optimization Approach for Wireless Sensor Networks Using Swarm Intelligence Optimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wang, Shuxin
      – PersonEntity:
          Name:
            NameFull: Zhang, Qingchen
      – PersonEntity:
          Name:
            NameFull: Zheng, Yejun
      – PersonEntity:
          Name:
            NameFull: Yue, Yinggao
      – PersonEntity:
          Name:
            NameFull: Cao, Li
      – PersonEntity:
          Name:
            NameFull: Xiong, Mengji
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 23137673
          Numbering:
            – Type: volume
              Value: 10
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Biomimetics (2313-7673)
              Type: main
ResultId 1