A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks
Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes acc...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/140391 |
| _version_ | 1826122605447348224 |
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| author | Chang, Yuchao Yuan, Xiaobing Li, Baoqing Niyato, Dusit Al-Dhahir, Naofal |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Chang, Yuchao Yuan, Xiaobing Li, Baoqing Niyato, Dusit Al-Dhahir, Naofal |
| author_sort | Chang, Yuchao |
| collection | NTU |
| description | Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with the state-of-the-art algorithms at an acceptable computational complexity. |
| first_indexed | 2024-10-01T05:50:53Z |
| format | Journal Article |
| id | ntu-10356/140391 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:50:53Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1403912020-05-28T08:48:17Z A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks Chang, Yuchao Yuan, Xiaobing Li, Baoqing Niyato, Dusit Al-Dhahir, Naofal School of Computer Science and Engineering Engineering::Computer science and engineering Unsupervised Learning Genetic Algorithms Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with the state-of-the-art algorithms at an acceptable computational complexity. 2020-05-28T08:48:12Z 2020-05-28T08:48:12Z 2018 Journal Article Chang, Y., Yuan, X., Li, B., Niyato, D., & Al-Dhahir, N. (2018). A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks. IEEE Communications Letters, 22(11), 2370-2373. doi:10.1109/LCOMM.2018.2870886 1089-7798 https://hdl.handle.net/10356/140391 10.1109/LCOMM.2018.2870886 2-s2.0-85053597824 11 22 2370 2373 en IEEE Communications Letters © 2018 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Unsupervised Learning Genetic Algorithms Chang, Yuchao Yuan, Xiaobing Li, Baoqing Niyato, Dusit Al-Dhahir, Naofal A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title | A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title_full | A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title_fullStr | A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title_full_unstemmed | A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title_short | A joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks |
| title_sort | joint unsupervised learning and genetic algorithm approach for topology control in energy efficient ultra dense wireless sensor networks |
| topic | Engineering::Computer science and engineering Unsupervised Learning Genetic Algorithms |
| url | https://hdl.handle.net/10356/140391 |
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