Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies
To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/8/965 |
| _version_ | 1797537259626430464 |
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| author | Amna Irshad Ziaul Haq Abbas Zaiwar Ali Ghulam Abbas Thar Baker Dhiya Al-Jumeily |
| author_facet | Amna Irshad Ziaul Haq Abbas Zaiwar Ali Ghulam Abbas Thar Baker Dhiya Al-Jumeily |
| author_sort | Amna Irshad |
| collection | DOAJ |
| description | To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes. |
| first_indexed | 2024-03-10T12:12:36Z |
| format | Article |
| id | doaj.art-7a2ee8296f314fdba3bd4ec86d830359 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T12:12:36Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-7a2ee8296f314fdba3bd4ec86d8303592023-11-21T16:05:48ZengMDPI AGElectronics2079-92922021-04-0110896510.3390/electronics10080965Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading PoliciesAmna Irshad0Ziaul Haq Abbas1Zaiwar Ali2Ghulam Abbas3Thar Baker4Dhiya Al-Jumeily5Telecommunications and Networking Research Center, GIK Institute of Engineering Sciences and Technology, Topi 23640, PakistanFaculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, PakistanFaculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, PakistanFaculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Topi 23640, PakistanDepartment of Computer Science, University of Sharjah, Sharjah 27272, United Arab EmiratesSchool of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 3AF, UKTo improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.https://www.mdpi.com/2079-9292/10/8/965mobile edge computingwireless energy transferwireless powered mobile edge computing |
| spellingShingle | Amna Irshad Ziaul Haq Abbas Zaiwar Ali Ghulam Abbas Thar Baker Dhiya Al-Jumeily Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies Electronics mobile edge computing wireless energy transfer wireless powered mobile edge computing |
| title | Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies |
| title_full | Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies |
| title_fullStr | Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies |
| title_full_unstemmed | Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies |
| title_short | Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies |
| title_sort | wireless powered mobile edge computing systems simultaneous time allocation and offloading policies |
| topic | mobile edge computing wireless energy transfer wireless powered mobile edge computing |
| url | https://www.mdpi.com/2079-9292/10/8/965 |
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