Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach
In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of Internet of Things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low...
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IEEE
2020-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/9001132/ |
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author | Asad Mahmood Ashfaq Ahmed Muhammad Naeem Yue Hong |
author_facet | Asad Mahmood Ashfaq Ahmed Muhammad Naeem Yue Hong |
author_sort | Asad Mahmood |
collection | DOAJ |
description | In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of Internet of Things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power Mobile Edge Cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and Mobile Edge Cloud (MEC) to address low power devices' battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes. |
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format | Article |
id | doaj.art-084fe6a7e5f542f2a6a4c19954c20186 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-16T07:03:25Z |
publishDate | 2020-01-01 |
publisher | IEEE |
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series | IEEE Access |
spelling | doaj.art-084fe6a7e5f542f2a6a4c19954c201862022-12-21T22:40:05ZengIEEEIEEE Access2169-35362020-01-018367573676310.1109/ACCESS.2020.29748099001132Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search ApproachAsad Mahmood0https://orcid.org/0000-0001-8978-3336Ashfaq Ahmed1https://orcid.org/0000-0002-6194-7551Muhammad Naeem2https://orcid.org/0000-0001-9734-4608Yue Hong3https://orcid.org/0000-0003-1022-0480College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad at Wah Campus, Wah Cantt, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad at Wah Campus, Wah Cantt, PakistanCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaIn the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of Internet of Things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power Mobile Edge Cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and Mobile Edge Cloud (MEC) to address low power devices' battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes.https://ieeexplore.ieee.org/document/9001132/Wireless Power TransferMobile Edge Cloud ComputingEergy Efficiency |
spellingShingle | Asad Mahmood Ashfaq Ahmed Muhammad Naeem Yue Hong Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach IEEE Access Wireless Power Transfer Mobile Edge Cloud Computing Eergy Efficiency |
title | Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach |
title_full | Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach |
title_fullStr | Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach |
title_full_unstemmed | Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach |
title_short | Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach |
title_sort | partial offloading in energy harvested mobile edge computing a direct search approach |
topic | Wireless Power Transfer Mobile Edge Cloud Computing Eergy Efficiency |
url | https://ieeexplore.ieee.org/document/9001132/ |
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