Green hybrid energy harvesting for intelligent mobile edge computing in internet of things

Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ge, Pingzheng [verfasserIn] Zhao, Junhui [verfasserIn] Zhang, Huan [verfasserIn] Zou, Dan [verfasserIn] Wang, Minjun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT

Übergeordnetes Werk:	Enthalten in: Physical communication - Amsterdam [u.a.] : Elsevier, 2008, 61
Übergeordnetes Werk:	volume:61

DOI / URN:	10.1016/j.phycom.2023.102171

Katalog-ID:	ELV066139902

Internformat


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520			\|a Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm.
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650		4	\|a Intelligent mobile edge computing
650		4	\|a IoT
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publishDate	2023
allfields	10.1016/j.phycom.2023.102171 doi (DE-627)ELV066139902 (ELSEVIER)S1874-4907(23)00174-X DE-627 ger DE-627 rda eng 530 620 VZ Ge, Pingzheng verfasserin aut Green hybrid energy harvesting for intelligent mobile edge computing in internet of things 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT Zhao, Junhui verfasserin (orcid)0000-0001-5958-6622 aut Zhang, Huan verfasserin aut Zou, Dan verfasserin aut Wang, Minjun verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 61 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:61 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61
spelling	10.1016/j.phycom.2023.102171 doi (DE-627)ELV066139902 (ELSEVIER)S1874-4907(23)00174-X DE-627 ger DE-627 rda eng 530 620 VZ Ge, Pingzheng verfasserin aut Green hybrid energy harvesting for intelligent mobile edge computing in internet of things 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT Zhao, Junhui verfasserin (orcid)0000-0001-5958-6622 aut Zhang, Huan verfasserin aut Zou, Dan verfasserin aut Wang, Minjun verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 61 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:61 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61
allfields_unstemmed	10.1016/j.phycom.2023.102171 doi (DE-627)ELV066139902 (ELSEVIER)S1874-4907(23)00174-X DE-627 ger DE-627 rda eng 530 620 VZ Ge, Pingzheng verfasserin aut Green hybrid energy harvesting for intelligent mobile edge computing in internet of things 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT Zhao, Junhui verfasserin (orcid)0000-0001-5958-6622 aut Zhang, Huan verfasserin aut Zou, Dan verfasserin aut Wang, Minjun verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 61 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:61 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61
allfieldsGer	10.1016/j.phycom.2023.102171 doi (DE-627)ELV066139902 (ELSEVIER)S1874-4907(23)00174-X DE-627 ger DE-627 rda eng 530 620 VZ Ge, Pingzheng verfasserin aut Green hybrid energy harvesting for intelligent mobile edge computing in internet of things 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT Zhao, Junhui verfasserin (orcid)0000-0001-5958-6622 aut Zhang, Huan verfasserin aut Zou, Dan verfasserin aut Wang, Minjun verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 61 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:61 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61
allfieldsSound	10.1016/j.phycom.2023.102171 doi (DE-627)ELV066139902 (ELSEVIER)S1874-4907(23)00174-X DE-627 ger DE-627 rda eng 530 620 VZ Ge, Pingzheng verfasserin aut Green hybrid energy harvesting for intelligent mobile edge computing in internet of things 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm. Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT Zhao, Junhui verfasserin (orcid)0000-0001-5958-6622 aut Zhang, Huan verfasserin aut Zou, Dan verfasserin aut Wang, Minjun verfasserin aut Enthalten in Physical communication Amsterdam [u.a.] : Elsevier, 2008 61 Online-Ressource (DE-627)573751552 (DE-600)2441929-1 (DE-576)294350721 1876-3219 nnns volume:61 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61
language	English
source	Enthalten in Physical communication 61 volume:61
sourceStr	Enthalten in Physical communication 61 volume:61
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Green computing Hybrid energy harvesting Intelligent mobile edge computing IoT
dewey-raw	530
isfreeaccess_bool	false
container_title	Physical communication
authorswithroles_txt_mv	Ge, Pingzheng @@aut@@ Zhao, Junhui @@aut@@ Zhang, Huan @@aut@@ Zou, Dan @@aut@@ Wang, Minjun @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	573751552
dewey-sort	3530
id	ELV066139902
language_de	englisch
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author	Ge, Pingzheng
spellingShingle	Ge, Pingzheng ddc 530 misc Green computing misc Hybrid energy harvesting misc Intelligent mobile edge computing misc IoT Green hybrid energy harvesting for intelligent mobile edge computing in internet of things
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topic	ddc 530 misc Green computing misc Hybrid energy harvesting misc Intelligent mobile edge computing misc IoT
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title	Green hybrid energy harvesting for intelligent mobile edge computing in internet of things
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title_full	Green hybrid energy harvesting for intelligent mobile edge computing in internet of things
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title_sort	green hybrid energy harvesting for intelligent mobile edge computing in internet of things
title_auth	Green hybrid energy harvesting for intelligent mobile edge computing in internet of things
abstract	Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm.
abstractGer	Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm.
abstract_unstemmed	Mobile device (MD) is usually energy constrained and the computation task will be interrupted when the battery power is run out. Intelligent mobile edge computing (MEC), as a promising technology in internet of things (IoT), can effectively save the computational energy of MD. Meanwhile, the hybrid energy harvesting (HEH) technology can enable MD to harvest available energy from the surrounding environment, so as to achieve the goal of green computing. According to the above mentioned, we integrate HEH technology into MEC to solve the limited energy problem of MD in this paper. Besides, to maximize the system utility (SU), a SU model with three measurable indicators of the latency, remaining energy and task success rate is proposed. Then, we improve the deep deterministic policy gradients (DDPG) algorithm and propose twin delayed deep deterministic policy gradient (TDDDPG) algorithm to obtain the suboptimal solution of proposed model. Eventually, the simulation results show that the TDDDPG can obtain the optimal SU compared with local computing (LC) algorithm, edge server computing (ESC) algorithm, random offloading (RO) algorithm and deep deterministic policy gradient (DDPG) algorithm.
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title_short	Green hybrid energy harvesting for intelligent mobile edge computing in internet of things
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author2	Zhao, Junhui Zhang, Huan Zou, Dan Wang, Minjun
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doi_str	10.1016/j.phycom.2023.102171
up_date	2024-07-06T16:43:11.162Z
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