面向无线体域网的任务卸载策略研究(任务书,开题报告,论文14000字)
摘要
现如今，云计算服务已满足不了各种新兴的生物医学所需要实现的高数据速率和低延迟的服务质量（QoS）要求，移动边缘计算（MEC）被认为是在考虑医院医疗场景时取代云计算服务的有效技术之一，因此本文基于物联网和移动边缘计算提出了一种新型医院病房医疗监测框架。
在该框架中，来自植入式生理信息采集传感器的计算密集型任务可以被体表可穿戴设备或MEC服务器进行有效的处理。在本文中，我们首先提出了一个无线中继使能任务卸载机制，该机制由一个网络模型和一个计算模型组成。此外，为了管理所有中继的计算资源和能量资源，给出了本地决策函数和卸载决策函数，并使用MATLAB软件仿真对整个系统的网络生命周期、吞吐量、继电器的剩余能量和路径损耗多个方面的性能进行了评估。仿真结果表明，所提出的基于优先级的任务卸载方案在多个方面的性能都优于现有的任务卸载方案。
本文所提出的网络框架可以应用于未来的医院医疗监测服务系统，如心血管疾病监测、移植器官监测等医疗场景。在未来可将该方案中无线通信和计算资源的综合权衡作为一个可研究的目标，使用常见的遗传算法、退火算法、凸优化和启发式等优化技术研究所提出模型的资源管理。
关键词：计算卸载；MEC；WBANs；资源管理
Abstract
Today, cloud computing services do not meet the high data rates and low latency quality of service (QoS) requirements required by a variety of emerging biomedical needs, and Mobile Edge Computing (MEC) is considered to be one of the effective technologies to replace cloud computing services when considering hospital medical scenarios.
Therefore, based on IoT and Mobile Edge Computing, this thesis presents a new medical monitoring framework for hospital wards. In this framework, compute-intensive tasks from implantable physiological information acquisition sensors can be effectively processed by wearable devices or MEC servers.In this thesis, we first propose a wireless relay enabling task to uninstall mechanism, which consists of a network model and a computational model. Moreover, in order to manage the computing resources and energy resources of all relays, the local decision function and unloading decision function are given, and MATLAB software simulation is used to evaluate the performance of the network lifecycle, throughput, residual energy and path loss of the whole system in four aspects. Simulation results show that the proposed priority-based task uninstall scheme achieves better performance than the existing task uninstall scheme in many aspects.
The network framework proposed in this paper can be applied to the future Hospital Medical monitoring service system, such as cardiovascular disease surveillance, transplant organ monitoring and other medical scenes.In the future, the comprehensive trade-off between wireless communication and computing resources in this scheme can be taken as a research goal, and the resource management of the model is proposed by using the common genetic algorithm, annealing algorithm, convex optimization and heuristic optimization technologies.
Key Words：computation offloading; MEC; WBANs;resource management
目录
第一章 绪论 1
1.1研究背景、目的与意义 1
1.2国内外研究现状 2
第二章 移动边缘计算相关技术和理论 4
2.1物联网概述 4
2.2移动边缘计算的基本概念及架构 5
2.3计算卸载技术概述 7
第三章 任务卸载策略 10
3.1系统结构 10
3.2计算模型 11
3.3路径损失模型 13
3.4资源管理方案 13
3.4.1网络初始化阶段 13
3.4.2本地决策过程 13
3.4.3任务卸载过程 14
第四章 仿真结果与分析 16
4.1网络拓扑结构 16
4.2性能评估 17
4.2.1网络生命周期 18
4.2.2吞吐量 19
4.2.3剩余能量 20
4.2.4路径损耗 21
第五章 结论 22
参考文献 23
致谢 25
|
