Intelligent ubiquitous computing for future UAV-enabled MEC network systems

Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a...
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Autor*in:	Chen, Lunyuan [verfasserIn] Zhao, Rui He, Ke Zhao, Zichao Fan, Liseng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Edge computing Unmanned aerial vehicles Jamming Reinforcement learning

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 25(2021), 4 vom: 02. Nov., Seite 2417-2427
Übergeordnetes Werk:	volume:25 ; year:2021 ; number:4 ; day:02 ; month:11 ; pages:2417-2427

Links:	Volltext

DOI / URN:	10.1007/s10586-021-03434-w

Katalog-ID:	SPR047582286

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allfields	10.1007/s10586-021-03434-w doi (DE-627)SPR047582286 (SPR)s10586-021-03434-w-e DE-627 ger DE-627 rakwb eng Chen, Lunyuan verfasserin aut Intelligent ubiquitous computing for future UAV-enabled MEC network systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Zhao, Rui aut He, Ke aut Zhao, Zichao aut Fan, Liseng aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 25(2021), 4 vom: 02. Nov., Seite 2417-2427 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427 https://dx.doi.org/10.1007/s10586-021-03434-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 4 02 11 2417-2427
spelling	10.1007/s10586-021-03434-w doi (DE-627)SPR047582286 (SPR)s10586-021-03434-w-e DE-627 ger DE-627 rakwb eng Chen, Lunyuan verfasserin aut Intelligent ubiquitous computing for future UAV-enabled MEC network systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Zhao, Rui aut He, Ke aut Zhao, Zichao aut Fan, Liseng aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 25(2021), 4 vom: 02. Nov., Seite 2417-2427 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427 https://dx.doi.org/10.1007/s10586-021-03434-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 4 02 11 2417-2427
allfields_unstemmed	10.1007/s10586-021-03434-w doi (DE-627)SPR047582286 (SPR)s10586-021-03434-w-e DE-627 ger DE-627 rakwb eng Chen, Lunyuan verfasserin aut Intelligent ubiquitous computing for future UAV-enabled MEC network systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Zhao, Rui aut He, Ke aut Zhao, Zichao aut Fan, Liseng aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 25(2021), 4 vom: 02. Nov., Seite 2417-2427 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427 https://dx.doi.org/10.1007/s10586-021-03434-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 4 02 11 2417-2427
allfieldsGer	10.1007/s10586-021-03434-w doi (DE-627)SPR047582286 (SPR)s10586-021-03434-w-e DE-627 ger DE-627 rakwb eng Chen, Lunyuan verfasserin aut Intelligent ubiquitous computing for future UAV-enabled MEC network systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Zhao, Rui aut He, Ke aut Zhao, Zichao aut Fan, Liseng aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 25(2021), 4 vom: 02. Nov., Seite 2417-2427 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427 https://dx.doi.org/10.1007/s10586-021-03434-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 4 02 11 2417-2427
allfieldsSound	10.1007/s10586-021-03434-w doi (DE-627)SPR047582286 (SPR)s10586-021-03434-w-e DE-627 ger DE-627 rakwb eng Chen, Lunyuan verfasserin aut Intelligent ubiquitous computing for future UAV-enabled MEC network systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Zhao, Rui aut He, Ke aut Zhao, Zichao aut Fan, Liseng aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 25(2021), 4 vom: 02. Nov., Seite 2417-2427 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427 https://dx.doi.org/10.1007/s10586-021-03434-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 4 02 11 2417-2427
language	English
source	Enthalten in Cluster computing 25(2021), 4 vom: 02. Nov., Seite 2417-2427 volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427
sourceStr	Enthalten in Cluster computing 25(2021), 4 vom: 02. Nov., Seite 2417-2427 volume:25 year:2021 number:4 day:02 month:11 pages:2417-2427
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Edge computing Unmanned aerial vehicles Jamming Reinforcement learning
isfreeaccess_bool	false
container_title	Cluster computing
authorswithroles_txt_mv	Chen, Lunyuan @@aut@@ Zhao, Rui @@aut@@ He, Ke @@aut@@ Zhao, Zichao @@aut@@ Fan, Liseng @@aut@@
publishDateDaySort_date	2021-11-02T00:00:00Z
hierarchy_top_id	320505332
id	SPR047582286
language_de	englisch
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author	Chen, Lunyuan
spellingShingle	Chen, Lunyuan misc Edge computing misc Unmanned aerial vehicles misc Jamming misc Reinforcement learning Intelligent ubiquitous computing for future UAV-enabled MEC network systems
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topic_title	Intelligent ubiquitous computing for future UAV-enabled MEC network systems Edge computing (dpeaa)DE-He213 Unmanned aerial vehicles (dpeaa)DE-He213 Jamming (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213
topic	misc Edge computing misc Unmanned aerial vehicles misc Jamming misc Reinforcement learning
topic_unstemmed	misc Edge computing misc Unmanned aerial vehicles misc Jamming misc Reinforcement learning
topic_browse	misc Edge computing misc Unmanned aerial vehicles misc Jamming misc Reinforcement learning
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Intelligent ubiquitous computing for future UAV-enabled MEC network systems
ctrlnum	(DE-627)SPR047582286 (SPR)s10586-021-03434-w-e
title_full	Intelligent ubiquitous computing for future UAV-enabled MEC network systems
author_sort	Chen, Lunyuan
journal	Cluster computing
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author_browse	Chen, Lunyuan Zhao, Rui He, Ke Zhao, Zichao Fan, Liseng
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format_se	Elektronische Aufsätze
author-letter	Chen, Lunyuan
doi_str_mv	10.1007/s10586-021-03434-w
title_sort	intelligent ubiquitous computing for future uav-enabled mec network systems
title_auth	Intelligent ubiquitous computing for future UAV-enabled MEC network systems
abstract	Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstractGer	Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
abstract_unstemmed	Abstract In this paper, we investigate intelligent ubiquitous computing for future unmanned aerial vehicle (UAV)-enabled mobile edge computing network (MEC) systems, where multiple users process some computational tasks with the help of one computational access point (CAP), under the jamming from a UAV attack. Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. Simulation results are provided to reveal that the proposed offloading strategy can outperform the conventional ones, and using transfer learning can achieve a better system performance while reducing the training time significantly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
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title_short	Intelligent ubiquitous computing for future UAV-enabled MEC network systems
url	https://dx.doi.org/10.1007/s10586-021-03434-w
remote_bool	true
author2	Zhao, Rui He, Ke Zhao, Zichao Fan, Liseng
author2Str	Zhao, Rui He, Ke Zhao, Zichao Fan, Liseng
ppnlink	320505332
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doi_str	10.1007/s10586-021-03434-w
up_date	2024-07-03T13:40:35.705Z
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Taking into account that the system may operate in a dynamic varying scenario, we optimize the system performance by using the reinforcement learning and transfer learning algorithms in order to reduce the latency and energy consumption. Specifically, we firstly use the reinforcement learning to devise the offloading strategy that meets the latency and energy consumption constraints as well as to alleviate the effect caused by jamming attack. We then propose to use the transfer learning to speed up the training process and improve the performance of reinforcement learning. 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