A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the...
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Gespeichert in:

Autor*in:	Yongli Xing [verfasserIn] Tao Ye [verfasserIn] Sami Ullah [verfasserIn] Muhammad Waqas [verfasserIn] Hisham Alasmary [verfasserIn] Zihui Liu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 18(2023), 2, p e0280468
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:2, p e0280468

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1371/journal.pone.0280468

Katalog-ID:	DOAJ088360024

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allfields	10.1371/journal.pone.0280468 doi (DE-627)DOAJ088360024 (DE-599)DOAJ64f9296e07364773959342b05eb572e0 DE-627 ger DE-627 rakwb eng Yongli Xing verfasserin aut A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms. Medicine R Science Q Tao Ye verfasserin aut Sami Ullah verfasserin aut Muhammad Waqas verfasserin aut Hisham Alasmary verfasserin aut Zihui Liu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 18(2023), 2, p e0280468 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:18 year:2023 number:2, p e0280468 https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/article/64f9296e07364773959342b05eb572e0 kostenfrei https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 2, p e0280468
spelling	10.1371/journal.pone.0280468 doi (DE-627)DOAJ088360024 (DE-599)DOAJ64f9296e07364773959342b05eb572e0 DE-627 ger DE-627 rakwb eng Yongli Xing verfasserin aut A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms. Medicine R Science Q Tao Ye verfasserin aut Sami Ullah verfasserin aut Muhammad Waqas verfasserin aut Hisham Alasmary verfasserin aut Zihui Liu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 18(2023), 2, p e0280468 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:18 year:2023 number:2, p e0280468 https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/article/64f9296e07364773959342b05eb572e0 kostenfrei https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 2, p e0280468
allfields_unstemmed	10.1371/journal.pone.0280468 doi (DE-627)DOAJ088360024 (DE-599)DOAJ64f9296e07364773959342b05eb572e0 DE-627 ger DE-627 rakwb eng Yongli Xing verfasserin aut A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms. Medicine R Science Q Tao Ye verfasserin aut Sami Ullah verfasserin aut Muhammad Waqas verfasserin aut Hisham Alasmary verfasserin aut Zihui Liu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 18(2023), 2, p e0280468 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:18 year:2023 number:2, p e0280468 https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/article/64f9296e07364773959342b05eb572e0 kostenfrei https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 2, p e0280468
allfieldsGer	10.1371/journal.pone.0280468 doi (DE-627)DOAJ088360024 (DE-599)DOAJ64f9296e07364773959342b05eb572e0 DE-627 ger DE-627 rakwb eng Yongli Xing verfasserin aut A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms. Medicine R Science Q Tao Ye verfasserin aut Sami Ullah verfasserin aut Muhammad Waqas verfasserin aut Hisham Alasmary verfasserin aut Zihui Liu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 18(2023), 2, p e0280468 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:18 year:2023 number:2, p e0280468 https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/article/64f9296e07364773959342b05eb572e0 kostenfrei https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 2, p e0280468
allfieldsSound	10.1371/journal.pone.0280468 doi (DE-627)DOAJ088360024 (DE-599)DOAJ64f9296e07364773959342b05eb572e0 DE-627 ger DE-627 rakwb eng Yongli Xing verfasserin aut A computational offloading optimization scheme based on deep reinforcement learning in perceptual network. 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms. Medicine R Science Q Tao Ye verfasserin aut Sami Ullah verfasserin aut Muhammad Waqas verfasserin aut Hisham Alasmary verfasserin aut Zihui Liu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 18(2023), 2, p e0280468 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:18 year:2023 number:2, p e0280468 https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/article/64f9296e07364773959342b05eb572e0 kostenfrei https://doi.org/10.1371/journal.pone.0280468 kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2023 2, p e0280468
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author	Yongli Xing
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topic_title	A computational offloading optimization scheme based on deep reinforcement learning in perceptual network
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title	A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.
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title_full	A computational offloading optimization scheme based on deep reinforcement learning in perceptual network
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title_sort	computational offloading optimization scheme based on deep reinforcement learning in perceptual network
title_auth	A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.
abstract	Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.
abstractGer	Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.
abstract_unstemmed	Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.
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title_short	A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.
url	https://doi.org/10.1371/journal.pone.0280468 https://doaj.org/article/64f9296e07364773959342b05eb572e0 https://doaj.org/toc/1932-6203
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up_date	2024-07-03T17:16:10.642Z
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A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.

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