Intelligent secure mobile edge computing for beyond 5G wireless networks
Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eave...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lai, Shiwei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions - Soroush Karimi Madahi, Seyed ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:45 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.phycom.2021.101283 |
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ELV053250966 |
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| 520 | |a Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. | ||
| 520 | |a Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. | ||
| 650 | 7 | |a Mobile edge computing |2 Elsevier | |
| 650 | 7 | |a Physical layer security |2 Elsevier | |
| 650 | 7 | |a Deep reinforcement learning |2 Elsevier | |
| 700 | 1 | |a Zhao, Rui |4 oth | |
| 700 | 1 | |a Tang, Shunpu |4 oth | |
| 700 | 1 | |a Xia, Junjuan |4 oth | |
| 700 | 1 | |a Zhou, Fasheng |4 oth | |
| 700 | 1 | |a Fan, Liseng |4 oth | |
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10.1016/j.phycom.2021.101283 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001313.pica (DE-627)ELV053250966 (ELSEVIER)S1874-4907(21)00020-3 DE-627 ger DE-627 rakwb eng 620 VZ 53.30 bkl Lai, Shiwei verfasserin aut Intelligent secure mobile edge computing for beyond 5G wireless networks 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Mobile edge computing Elsevier Physical layer security Elsevier Deep reinforcement learning Elsevier Zhao, Rui oth Tang, Shunpu oth Xia, Junjuan oth Zhou, Fasheng oth Fan, Liseng oth Enthalten in Elsevier Soroush Karimi Madahi, Seyed ELSEVIER A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions 2022 Amsterdam [u.a.] (DE-627)ELV008049807 volume:45 year:2021 pages:0 https://doi.org/10.1016/j.phycom.2021.101283 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 53.30 Elektrische Energietechnik: Allgemeines VZ AR 45 2021 0 |
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10.1016/j.phycom.2021.101283 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001313.pica (DE-627)ELV053250966 (ELSEVIER)S1874-4907(21)00020-3 DE-627 ger DE-627 rakwb eng 620 VZ 53.30 bkl Lai, Shiwei verfasserin aut Intelligent secure mobile edge computing for beyond 5G wireless networks 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Mobile edge computing Elsevier Physical layer security Elsevier Deep reinforcement learning Elsevier Zhao, Rui oth Tang, Shunpu oth Xia, Junjuan oth Zhou, Fasheng oth Fan, Liseng oth Enthalten in Elsevier Soroush Karimi Madahi, Seyed ELSEVIER A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions 2022 Amsterdam [u.a.] (DE-627)ELV008049807 volume:45 year:2021 pages:0 https://doi.org/10.1016/j.phycom.2021.101283 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 53.30 Elektrische Energietechnik: Allgemeines VZ AR 45 2021 0 |
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10.1016/j.phycom.2021.101283 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001313.pica (DE-627)ELV053250966 (ELSEVIER)S1874-4907(21)00020-3 DE-627 ger DE-627 rakwb eng 620 VZ 53.30 bkl Lai, Shiwei verfasserin aut Intelligent secure mobile edge computing for beyond 5G wireless networks 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Mobile edge computing Elsevier Physical layer security Elsevier Deep reinforcement learning Elsevier Zhao, Rui oth Tang, Shunpu oth Xia, Junjuan oth Zhou, Fasheng oth Fan, Liseng oth Enthalten in Elsevier Soroush Karimi Madahi, Seyed ELSEVIER A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions 2022 Amsterdam [u.a.] (DE-627)ELV008049807 volume:45 year:2021 pages:0 https://doi.org/10.1016/j.phycom.2021.101283 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 53.30 Elektrische Energietechnik: Allgemeines VZ AR 45 2021 0 |
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10.1016/j.phycom.2021.101283 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001313.pica (DE-627)ELV053250966 (ELSEVIER)S1874-4907(21)00020-3 DE-627 ger DE-627 rakwb eng 620 VZ 53.30 bkl Lai, Shiwei verfasserin aut Intelligent secure mobile edge computing for beyond 5G wireless networks 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Mobile edge computing Elsevier Physical layer security Elsevier Deep reinforcement learning Elsevier Zhao, Rui oth Tang, Shunpu oth Xia, Junjuan oth Zhou, Fasheng oth Fan, Liseng oth Enthalten in Elsevier Soroush Karimi Madahi, Seyed ELSEVIER A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions 2022 Amsterdam [u.a.] (DE-627)ELV008049807 volume:45 year:2021 pages:0 https://doi.org/10.1016/j.phycom.2021.101283 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 53.30 Elektrische Energietechnik: Allgemeines VZ AR 45 2021 0 |
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10.1016/j.phycom.2021.101283 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001313.pica (DE-627)ELV053250966 (ELSEVIER)S1874-4907(21)00020-3 DE-627 ger DE-627 rakwb eng 620 VZ 53.30 bkl Lai, Shiwei verfasserin aut Intelligent secure mobile edge computing for beyond 5G wireless networks 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. Mobile edge computing Elsevier Physical layer security Elsevier Deep reinforcement learning Elsevier Zhao, Rui oth Tang, Shunpu oth Xia, Junjuan oth Zhou, Fasheng oth Fan, Liseng oth Enthalten in Elsevier Soroush Karimi Madahi, Seyed ELSEVIER A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions 2022 Amsterdam [u.a.] (DE-627)ELV008049807 volume:45 year:2021 pages:0 https://doi.org/10.1016/j.phycom.2021.101283 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 53.30 Elektrische Energietechnik: Allgemeines VZ AR 45 2021 0 |
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Enthalten in A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions Amsterdam [u.a.] volume:45 year:2021 pages:0 |
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A new DFT-based frequency estimation algorithm for protection devices under normal and fault conditions |
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Lai, Shiwei @@aut@@ Zhao, Rui @@oth@@ Tang, Shunpu @@oth@@ Xia, Junjuan @@oth@@ Zhou, Fasheng @@oth@@ Fan, Liseng @@oth@@ |
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In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. 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Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. |
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Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. |
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Computational task offloading at the mobile edge servers is a promising strategy to reduce latency and energy consumption in 5G wireless networks. In this paper, we study the problem of offloading decision and system design in the intelligent secure mobile edge computing (MEC) system with a UAV eavesdropper, where the eavesdropper can overhear the secure computational task from the user to the computational access point (CAP). The proposed framework is aimed to ensure the physical-layer security and decrease the latency and energy consumption in communication and computation. We firstly formulate the optimization of the MEC networks as a multi-objective optimization problem and then use a linear combination of the latency and energy consumption to measure the system cost. We devise an adaptive offloading strategy by incorporating the wireless bandwidth allocation and the transmit power allocation among users through the deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network (DQN) based strategy to automatically solve the optimization problem by designing the system state, action, and the reward function at detail. At last, we demonstrate the usefulness of the considered intelligent offloading strategy for the design of the MEC networks by comparing with the other schemes. The proposed strategy can perform significant system cost saving of the considered MEC networks. |
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