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Joint Task Offloading and Resource Allocation in Mobile Edge Computing-Enabled Medical Vehicular Networks

Gorde:
Argitaratua izan da:Mathematics vol. 13, no. 1 (2025), p. 52
Egile nagusia: Zhang, Chuangchuang
Beste egile batzuk: Liu, Siquan, Yang, Hongyong, Cui, Guanghai, Li, Fuliang, Wang, Xingwei
Argitaratua:
MDPI AG
Gaiak:
Optimization
Edge computing
Resource allocation
Greedy algorithms
Mobile computing
User experience
Multiple objective analysis
Health services
Performance evaluation
Energy consumption
Optimization models
Vehicles
Patients
Delay
Quality of service architectures
Genetic algorithms
Computation offloading
Design
Energy efficiency
Quality of service
Linear programming
Networks
Cloud computing
Completion time
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Laburpena: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.
ISSN:2227-7390
DOI:10.3390/math13010052
Baliabidea:Engineering Database