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...

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Main Authors: Chang, Yuchao, Yuan, Xiaobing, Li, Baoqing, Niyato, Dusit, Al-Dhahir, Naofal
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140391
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Unsupervised Learning
Genetic Algorithms
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
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.
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
format Article
author Chang, Yuchao
Yuan, Xiaobing
Li, Baoqing
Niyato, Dusit
Al-Dhahir, Naofal
author_sort Chang, Yuchao
title 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_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_sort joint unsupervised learning and genetic algorithm approach for topology control in energy-efficient ultra-dense wireless sensor networks
publishDate 2020
url https://hdl.handle.net/10356/140391
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