Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks
In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency,...
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7711 |
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| author | Zhen Wang Jin Duan Haobo Xu Xue Song Yang Yang |
| author_facet | Zhen Wang Jin Duan Haobo Xu Xue Song Yang Yang |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>. These nodes were categorized into four heterogeneous scenarios: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>0</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.1</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>2</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.2</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>3</mn></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.3</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>1.5</mn></mrow></semantics></math></inline-formula>. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects. |
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| spelling | doaj.art-1ad9c5a06291497b9f6a81871f0df8e82023-11-19T12:53:07ZengMDPI AGSensors1424-82202023-09-012318771110.3390/s23187711Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor NetworksZhen Wang0Jin Duan1Haobo Xu2Xue Song3Yang Yang4School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, ChinaIn the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>. These nodes were categorized into four heterogeneous scenarios: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>0</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.1</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>2</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.2</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>3</mn></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mo>=</mo><mn>0.3</mn><mo>,</mo><mi>α</mi><mo>=</mo><mn>1.5</mn></mrow></semantics></math></inline-formula>. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.https://www.mdpi.com/1424-8220/23/18/7711heterogeneous wireless sensor networkspelican optimization algorithmenergy efficientcluster head selection |
| spellingShingle | Zhen Wang Jin Duan Haobo Xu Xue Song Yang Yang Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks Sensors heterogeneous wireless sensor networks pelican optimization algorithm energy efficient cluster head selection |
| title | Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks |
| title_full | Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks |
| title_fullStr | Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks |
| title_full_unstemmed | Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks |
| title_short | Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks |
| title_sort | enhanced pelican optimization algorithm for cluster head selection in heterogeneous wireless sensor networks |
| topic | heterogeneous wireless sensor networks pelican optimization algorithm energy efficient cluster head selection |
| url | https://www.mdpi.com/1424-8220/23/18/7711 |
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