Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks
In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients....
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/52 |
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| author | Chuangchuang Zhang Siquan Liu Hongyong Yang Guanghai Cui Fuliang Li Xingwei Wang |
| author_facet | Chuangchuang Zhang Siquan Liu Hongyong Yang Guanghai Cui Fuliang Li Xingwei Wang |
| author_sort | Chuangchuang Zhang |
| collection | DOAJ |
| description | In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption. |
| format | Article |
| id | doaj-art-d23d952325c84629aef16f4866ae6be3 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d23d952325c84629aef16f4866ae6be32025-01-10T13:18:05ZengMDPI AGMathematics2227-73902024-12-011315210.3390/math13010052Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular NetworksChuangchuang Zhang0Siquan Liu1Hongyong Yang2Guanghai Cui3Fuliang Li4Xingwei Wang5School of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaSchool of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaSchool of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaSchool of Information and Electrical Engineering, Ludong University, Yantai 264025, ChinaCollege of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaCollege of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaIn medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.https://www.mdpi.com/2227-7390/13/1/52medical vehicular networksMECtask offloadingresource allocationmulti-objective optimization |
| spellingShingle | Chuangchuang Zhang Siquan Liu Hongyong Yang Guanghai Cui Fuliang Li Xingwei Wang Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks Mathematics medical vehicular networks MEC task offloading resource allocation multi-objective optimization |
| title | Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks |
| title_full | Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks |
| title_fullStr | Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks |
| title_full_unstemmed | Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks |
| title_short | Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks |
| title_sort | joint task offloading and resource allocation in mobile edge computing enabled medical vehicular networks |
| topic | medical vehicular networks MEC task offloading resource allocation multi-objective optimization |
| url | https://www.mdpi.com/2227-7390/13/1/52 |
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