Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks
Strategic sensor node deployment is crucial for optimizing the performance of Wireless Sensor Networks (WSN). This optimization enhances the network coverage, data acquisition efficiency, and energy conservation, ultimately prolonging the network lifetime and improving overall system reliability. Th...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025019073 |
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| author | Anjana Koyalil Sivacoumar Rajalingam |
| author_facet | Anjana Koyalil Sivacoumar Rajalingam |
| author_sort | Anjana Koyalil |
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| description | Strategic sensor node deployment is crucial for optimizing the performance of Wireless Sensor Networks (WSN). This optimization enhances the network coverage, data acquisition efficiency, and energy conservation, ultimately prolonging the network lifetime and improving overall system reliability. Therefore, researchers are paying more attention to improving the network lifetime. However, existing methods often fall short of achieving optimal network coverage due to inadequate sensor deployment. Additionally, the absence of clustering in certain models compromises the network capacity. To tackle these challenges, a new multi-level clustering technique with a heuristic optimization algorithm is proposed in this research work. A novel heuristic, the Modernized Pufferfish Optimization Algorithm (MPOA), is introduced to optimize WSN performance, drawing inspiration from the pufferfish's natural defense strategies. Initially, an enhanced multi-level K-means algorithm (MKA) is employed to cluster the nodes dynamically in response to evolving network conditions. Finally, an MPOA is suggested for selecting the most suitable Cluster Head (CH) by introducing a new random parameter to solve premature convergence and to obtain the global optimum solution. The test results show that the proposed strategy significantly improves the CH selection, increases the network lifetime by 12.5 % to 62.5 %, decreases the energy consumption by 16.5 % to 28.7 %, decreases the path loss by 16 % to 46 %, increases the number of alive nodes by 32.5 % to 55 %, increases the residual energy by 3.3 % to 77.7 %, decreases the cost function by 1.76 % to 4.42 %, and increases the normalized energy, compared to several benchmark techniques based on varying number of nodes and rounds. |
| format | Article |
| id | doaj-art-b57c3ce2fa484907b83cdb86b11bf486 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| spelling | doaj-art-b57c3ce2fa484907b83cdb86b11bf4862025-08-20T03:30:02ZengElsevierResults in Engineering2590-12302025-09-012710583610.1016/j.rineng.2025.105836Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networksAnjana Koyalil0Sivacoumar Rajalingam1Department of Sensors and Bio-medical Technology, School of Electronics, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, IndiaCorresponding author.; Department of Sensors and Bio-medical Technology, School of Electronics, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, IndiaStrategic sensor node deployment is crucial for optimizing the performance of Wireless Sensor Networks (WSN). This optimization enhances the network coverage, data acquisition efficiency, and energy conservation, ultimately prolonging the network lifetime and improving overall system reliability. Therefore, researchers are paying more attention to improving the network lifetime. However, existing methods often fall short of achieving optimal network coverage due to inadequate sensor deployment. Additionally, the absence of clustering in certain models compromises the network capacity. To tackle these challenges, a new multi-level clustering technique with a heuristic optimization algorithm is proposed in this research work. A novel heuristic, the Modernized Pufferfish Optimization Algorithm (MPOA), is introduced to optimize WSN performance, drawing inspiration from the pufferfish's natural defense strategies. Initially, an enhanced multi-level K-means algorithm (MKA) is employed to cluster the nodes dynamically in response to evolving network conditions. Finally, an MPOA is suggested for selecting the most suitable Cluster Head (CH) by introducing a new random parameter to solve premature convergence and to obtain the global optimum solution. The test results show that the proposed strategy significantly improves the CH selection, increases the network lifetime by 12.5 % to 62.5 %, decreases the energy consumption by 16.5 % to 28.7 %, decreases the path loss by 16 % to 46 %, increases the number of alive nodes by 32.5 % to 55 %, increases the residual energy by 3.3 % to 77.7 %, decreases the cost function by 1.76 % to 4.42 %, and increases the normalized energy, compared to several benchmark techniques based on varying number of nodes and rounds.http://www.sciencedirect.com/science/article/pii/S2590123025019073Wireless sensor networkMultilevel K-means algorithmModernized pufferfish optimization algorithmCluster head selectionNetwork lifetime optimization |
| spellingShingle | Anjana Koyalil Sivacoumar Rajalingam Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks Results in Engineering Wireless sensor network Multilevel K-means algorithm Modernized pufferfish optimization algorithm Cluster head selection Network lifetime optimization |
| title | Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| title_full | Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| title_fullStr | Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| title_full_unstemmed | Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| title_short | Enhanced multi-level K-means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| title_sort | enhanced multi level k means clustering and cluster head selection using a modernized pufferfish optimization algorithm for lifetime maximization in wireless sensor networks |
| topic | Wireless sensor network Multilevel K-means algorithm Modernized pufferfish optimization algorithm Cluster head selection Network lifetime optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025019073 |
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