Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks
Wedi'i Gadw mewn:
| Cyhoeddwyd yn: | Mathematics vol. 13, no. 1 (2025), p. 52 |
|---|---|
| Prif Awdur: | |
| Awduron Eraill: | , , , , |
| Cyhoeddwyd: |
MDPI AG
|
| Pynciau: | |
| Mynediad Ar-lein: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3153862558 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13010052 |2 doi | |
| 035 | |a 3153862558 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Zhang, Chuangchuang |u School of Information and Electrical Engineering, Ludong University, Yantai 264025, China | |
| 245 | 1 | |a Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Resource allocation | ||
| 653 | |a Greedy algorithms | ||
| 653 | |a Mobile computing | ||
| 653 | |a User experience | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Health services | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Energy consumption | ||
| 653 | |a Optimization models | ||
| 653 | |a Vehicles | ||
| 653 | |a Patients | ||
| 653 | |a Delay | ||
| 653 | |a Quality of service architectures | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Computation offloading | ||
| 653 | |a Design | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Quality of service | ||
| 653 | |a Linear programming | ||
| 653 | |a Networks | ||
| 653 | |a Cloud computing | ||
| 653 | |a Completion time | ||
| 700 | 1 | |a Liu, Siquan |u School of Information and Electrical Engineering, Ludong University, Yantai 264025, China | |
| 700 | 1 | |a Yang, Hongyong |u School of Information and Electrical Engineering, Ludong University, Yantai 264025, China | |
| 700 | 1 | |a Cui, Guanghai |u School of Information and Electrical Engineering, Ludong University, Yantai 264025, China | |
| 700 | 1 | |a Li, Fuliang |u College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China | |
| 700 | 1 | |a Wang, Xingwei |u College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 1 (2025), p. 52 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3153862558/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3153862558/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3153862558/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |