An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks
The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to ac...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Baghdad, College of Science for Women
2024-02-01
|
| Series: | مجلة بغداد للعلوم |
| Subjects: | |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9707 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849414561712570368 |
|---|---|
| author | Sen-Yu Yang Yin-Hong Xiang Di-Wen Kang Kai-Qing Zhou |
| author_facet | Sen-Yu Yang Yin-Hong Xiang Di-Wen Kang Kai-Qing Zhou |
| author_sort | Sen-Yu Yang |
| collection | DOAJ |
| description |
The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others.
|
| format | Article |
| id | doaj-art-9a7de2e5f8384a1c89f58791221057a9 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-9a7de2e5f8384a1c89f58791221057a92025-08-20T03:33:46ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862024-02-01212(SI)10.21123/bsj.2024.9707An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor NetworksSen-Yu Yang0Yin-Hong Xiang1Di-Wen Kang2Kai-Qing Zhou3School of Communication and Electronic Engineering, Jishou University, Jishou, 416000, Hunan, China.School of Communication and Electronic Engineering, Jishou University, Jishou, 416000, Hunan, China.School of Communication and Electronic Engineering, Jishou University, Jishou, 416000, Hunan, China.School of Communication and Electronic Engineering, Jishou University, Jishou, 416000, Hunan, China. The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9707cuckoo search algorithm, dimension-by-dimension update, opposition-based learning, wireless sensor network, PSO operator |
| spellingShingle | Sen-Yu Yang Yin-Hong Xiang Di-Wen Kang Kai-Qing Zhou An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks مجلة بغداد للعلوم cuckoo search algorithm, dimension-by-dimension update, opposition-based learning, wireless sensor network, PSO operator |
| title | An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks |
| title_full | An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks |
| title_fullStr | An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks |
| title_full_unstemmed | An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks |
| title_short | An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks |
| title_sort | improved cuckoo search algorithm for maximizing the coverage range of wireless sensor networks |
| topic | cuckoo search algorithm, dimension-by-dimension update, opposition-based learning, wireless sensor network, PSO operator |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9707 |
| work_keys_str_mv | AT senyuyang animprovedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT yinhongxiang animprovedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT diwenkang animprovedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT kaiqingzhou animprovedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT senyuyang improvedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT yinhongxiang improvedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT diwenkang improvedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks AT kaiqingzhou improvedcuckoosearchalgorithmformaximizingthecoveragerangeofwirelesssensornetworks |