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Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT.

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Title: Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT.
Authors: Zhang, Yawen, Miao, Yifeng, Pan, Shujia, Chen, Siguang
Source: Security & Communication Networks; 12/27/2021, p1-10, 10p
Subject Terms: ENERGY harvesting, MOBILE computing, INTERNET of things, GREEDY algorithms, COMPUTER systems, DEEP learning, TIME management
Abstract: In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Security & Communication Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Yawen%22">Zhang, Yawen</searchLink><br /><searchLink fieldCode="AR" term="%22Miao%2C+Yifeng%22">Miao, Yifeng</searchLink><br /><searchLink fieldCode="AR" term="%22Pan%2C+Shujia%22">Pan, Shujia</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Siguang%22">Chen, Siguang</searchLink>
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  Data: Security & Communication Networks; 12/27/2021, p1-10, 10p
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  Data: <searchLink fieldCode="DE" term="%22ENERGY+harvesting%22">ENERGY harvesting</searchLink><br /><searchLink fieldCode="DE" term="%22MOBILE+computing%22">MOBILE computing</searchLink><br /><searchLink fieldCode="DE" term="%22INTERNET+of+things%22">INTERNET of things</searchLink><br /><searchLink fieldCode="DE" term="%22GREEDY+algorithms%22">GREEDY algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+systems%22">COMPUTER systems</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22TIME+management%22">TIME management</searchLink>
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  Data: In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Security & Communication Networks is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/2021/8167796
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      – Code: eng
        Text: English
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        PageCount: 10
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      – SubjectFull: ENERGY harvesting
        Type: general
      – SubjectFull: MOBILE computing
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      – SubjectFull: INTERNET of things
        Type: general
      – SubjectFull: GREEDY algorithms
        Type: general
      – SubjectFull: COMPUTER systems
        Type: general
      – SubjectFull: DEEP learning
        Type: general
      – SubjectFull: TIME management
        Type: general
    Titles:
      – TitleFull: Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT.
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            NameFull: Zhang, Yawen
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            NameFull: Chen, Siguang
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          Dates:
            – D: 27
              M: 12
              Text: 12/27/2021
              Type: published
              Y: 2021
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