Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks
The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall sy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002008 |
| _version_ | 1797663907248078848 |
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| author | Manzoor Ahmed Haya Mesfer Alshahrani Nuha Alruwais Mashael M. Asiri Mesfer Al Duhayyim Wali Ullah Khan Tahir khurshaid Ali Nauman |
| author_facet | Manzoor Ahmed Haya Mesfer Alshahrani Nuha Alruwais Mashael M. Asiri Mesfer Al Duhayyim Wali Ullah Khan Tahir khurshaid Ali Nauman |
| author_sort | Manzoor Ahmed |
| collection | DOAJ |
| description | The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches. |
| first_indexed | 2024-03-11T19:21:35Z |
| format | Article |
| id | doaj.art-e56a30f0bbdd475c9d5aa7bb7df2c738 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T19:21:35Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-e56a30f0bbdd475c9d5aa7bb7df2c7382023-10-07T04:33:56ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-09-01358101646Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networksManzoor Ahmed0Haya Mesfer Alshahrani1Nuha Alruwais2Mashael M. Asiri3Mesfer Al Duhayyim4Wali Ullah Khan5Tahir khurshaid6Ali Nauman7School of Computer and Information Science and also with Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan City 432000, ChinaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Saudi Arabia, P.O. Box 22459, Riyadh 11495, Saudi ArabiaDepartment of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi ArabiaInterdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg City, LuxembourgDepartment of Electrical Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaDepartment of Information and Communication Engineering, Yeungnam University, Republic of Korea; Corresponding author.The rapid evolution of communication systems towards the next generation has led to an increased deployment of Internet of Things (IoT) devices for various real-time applications. However, these devices often face limitations in terms of processing power and battery life, which can hinder overall system performance. Additionally, applications such as augmented reality and surveillance require intensive computations within tight timeframes. This research focuses on investigating a mobile edge computing (MEC) network empowered by unmanned aerial vehicle intelligent reflecting surfaces (UAV-IRS) to enhance the computational energy efficiency of the system through optimized resource allocation. The MEC infrastructure incorporates the energy transfer circuit (ETC) and edge server (ES), co-located with the intelligent access point (AP). To eliminate interference between energy transfer and data transmission, a time-division multiple access method is utilized. In the first phase, the ETC wirelessly transfers power to low-power IoT devices, which efficiently harvest and store the received energy in their batteries. In the second phase, IoT devices employ the stored energy for local computing or offloading tasks. Furthermore, the presence of tall buildings may obstruct communication routes, impacting system functionality. To address these challenges, we propose an optimization framework that simultaneously considers time, power, phase shift design, and local computational resources. This joint optimization problem is non-convex and non-linear, making it NP-hard. To tackle this complexity, we decompose the problem into subproblems and solve them iteratively using a convex optimization toolbox like CVX. Through simulations, we demonstrate that our proposed optimization framework significantly improves 40.7% system performance compared to alternative approaches.http://www.sciencedirect.com/science/article/pii/S1319157823002008Energy consumption minimizationIntelligent reflecting surfacesLatencyMathematical optimizationMobile edge computingResource allocation |
| spellingShingle | Manzoor Ahmed Haya Mesfer Alshahrani Nuha Alruwais Mashael M. Asiri Mesfer Al Duhayyim Wali Ullah Khan Tahir khurshaid Ali Nauman Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks Journal of King Saud University: Computer and Information Sciences Energy consumption minimization Intelligent reflecting surfaces Latency Mathematical optimization Mobile edge computing Resource allocation |
| title | Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks |
| title_full | Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks |
| title_fullStr | Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks |
| title_full_unstemmed | Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks |
| title_short | Joint optimization of UAV-IRS placement and resource allocation for wireless powered mobile edge computing networks |
| title_sort | joint optimization of uav irs placement and resource allocation for wireless powered mobile edge computing networks |
| topic | Energy consumption minimization Intelligent reflecting surfaces Latency Mathematical optimization Mobile edge computing Resource allocation |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002008 |
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