View in EDS

Node Localization in 3D WSN Using Optimized Deep Learning Mechanism.

Saved in:
Bibliographic Details
Title: Node Localization in 3D WSN Using Optimized Deep Learning Mechanism.
Authors: Raghuvanshi, Akash1 (AUTHOR) akash.raghuvanshi@gmail.com, Kumar, Awadhesh1 (AUTHOR) awadhesh@knit.ac.in, Chandra, Nilesh1 (AUTHOR) nilesh.chandra08@gmail.com
Source: International Journal of Communication Systems. Feb2026, Vol. 39 Issue 3, p1-23. 23p.
Subjects: Wireless sensor networks, Deep learning, Long short-term memory, Wireless localization, Algorithms, Artificial neural networks
Abstract: Many mobile and sensor nodes comprised wireless sensor networks (WSN). Yet, it is quite challenging to locate these sensor and mobile nodes. Because of the time‐varying movements, analysis of the current positions of sensor nodes in WSN is quite challenging. Because of locating all known sources in unknown nodes, the typical localization approaches are used to find the position of these nodes, producing a lot of inaccuracy when forecasting the distance between the source and unknown nodes. Also, it is very expensive to use Global Positioning System (GPS) technology for node detection. Although numerous localization procedures for WSNs in a three‐dimensional topology have been proposed, it is still important to create and refine new localization algorithms to further increase the accuracy of the node positioning method. In this research work, an advanced heuristic algorithm and a deep learning technique are developed for localizing the unknown nodes in a three‐dimensional wireless sensor network (3D‐WSN). Initially, the distance between the unknown node as well as the anchor node is evaluated using efficient hybrid deep learning techniques named bidirectional long short‐term memory (Bi‐LSTM) and gated recurrent unit (GRU). Hybrid position of mine blast and chameleon swarm (HP‐MBCS) is developed for tuning the parameters in deep learning techniques. An objective function of minimizing the average localization error (ALE) on node localization is obtained by optimally selecting the position of unknown nodes with the support of computed distance from the developed Bi‐LSTM‐GRU technique. The experimental simulation is carried out between the proposed and traditional models to show that the proposed model is efficient in minimizing localization error. The resultant outcome shows that the MEP value of the proposed HP‐MBCS‐Bi‐LSTM‐GRU model is 24.191, which is better than the other existing algorithms like EHO, EOO, MBO, and CSO, respectively. Thus, it was confirmed that the proposed Bi‐LSTM‐GRU not only improves the precision and effectiveness of node localization but also enhances the overall energy efficiency. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 190860131
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Node Localization in 3D WSN Using Optimized Deep Learning Mechanism.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Raghuvanshi%2C+Akash%22">Raghuvanshi, Akash</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akash.raghuvanshi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Awadhesh%22">Kumar, Awadhesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> awadhesh@knit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Chandra%2C+Nilesh%22">Chandra, Nilesh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nilesh.chandra08@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Communication+Systems%22">International Journal of Communication Systems</searchLink>. Feb2026, Vol. 39 Issue 3, p1-23. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Wireless+sensor+networks%22">Wireless sensor networks</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Wireless+localization%22">Wireless localization</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Many mobile and sensor nodes comprised wireless sensor networks (WSN). Yet, it is quite challenging to locate these sensor and mobile nodes. Because of the time‐varying movements, analysis of the current positions of sensor nodes in WSN is quite challenging. Because of locating all known sources in unknown nodes, the typical localization approaches are used to find the position of these nodes, producing a lot of inaccuracy when forecasting the distance between the source and unknown nodes. Also, it is very expensive to use Global Positioning System (GPS) technology for node detection. Although numerous localization procedures for WSNs in a three‐dimensional topology have been proposed, it is still important to create and refine new localization algorithms to further increase the accuracy of the node positioning method. In this research work, an advanced heuristic algorithm and a deep learning technique are developed for localizing the unknown nodes in a three‐dimensional wireless sensor network (3D‐WSN). Initially, the distance between the unknown node as well as the anchor node is evaluated using efficient hybrid deep learning techniques named bidirectional long short‐term memory (Bi‐LSTM) and gated recurrent unit (GRU). Hybrid position of mine blast and chameleon swarm (HP‐MBCS) is developed for tuning the parameters in deep learning techniques. An objective function of minimizing the average localization error (ALE) on node localization is obtained by optimally selecting the position of unknown nodes with the support of computed distance from the developed Bi‐LSTM‐GRU technique. The experimental simulation is carried out between the proposed and traditional models to show that the proposed model is efficient in minimizing localization error. The resultant outcome shows that the MEP value of the proposed HP‐MBCS‐Bi‐LSTM‐GRU model is 24.191, which is better than the other existing algorithms like EHO, EOO, MBO, and CSO, respectively. Thus, it was confirmed that the proposed Bi‐LSTM‐GRU not only improves the precision and effectiveness of node localization but also enhances the overall energy efficiency. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Communication Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=190860131
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/dac.70377
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1
    Subjects:
      – SubjectFull: Wireless sensor networks
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Wireless localization
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
    Titles:
      – TitleFull: Node Localization in 3D WSN Using Optimized Deep Learning Mechanism.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Raghuvanshi, Akash
      – PersonEntity:
          Name:
            NameFull: Kumar, Awadhesh
      – PersonEntity:
          Name:
            NameFull: Chandra, Nilesh
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10745351
          Numbering:
            – Type: volume
              Value: 39
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: International Journal of Communication Systems
              Type: main
ResultId 1