三大基本要求:
看了很多论文,其introduction部分一般是以如下内容排版方式进行写作:
论文研究领域的背景知识,引入研究课题
背景知识
ALMOST all electronic devices, such as home appliances, automotive electronics, office facilities, customer,electronics, various sensors and actuators are expected to be connected to the Internet in future, and thus form the Internet of Things (IoT). Boosted by big data, IoT will greatly facilitate our daily life and make services more intelligent, such as smart home, transportation, health-care, manufacture, and factory [1].
研究课题
To earn the welfare of the IoT, the cloud computing and cloud storage infrastructure that can provide sufficient processing and storage resources are considered as the enabler for ubiquitous IoT services. However, with the rapid increasing popularity of emerging applications, such as augmented reality, face and voice recognition, wearable computing, image and video processing, the centralized cloud-based IoT raises concerns [2], such as latency due to long distance between user terminal (things) and the cloud, and bandwidth limitation due to the limited capacity of the backhaul link.
强调该研究的重要性,并且回顾前人的研究结果,指出以前研究结果的不足
强调该研究的重要性
Fog computing and caching that move the network resources from the cloud to the edge has been proposed to accommodate future IoT services, and the fog-enabled IoT is considered as a promising network architecture [3]. ....... Therefore, the efficient management and synergy of caching, computing, and radio resources become the challenges for the fog-enabled IoT.
指出以前研究结果的不足
Although many works (reviewed in Section II of this paper) have been dedicated to providing optimal computation offloading strategies, content caching policies and resource allocation scheme. However, these important issues, especially computing and caching, were usually considered and solved separately in most existing works. ...
介绍以前的解决方案,陈述作者的研究基础。
介绍以前的解决方案,带了[1],[2]...都是引述前人的研究
Most of existing solutions formulate the computation offloading or content caching as the constrained convex optimization problems with different selected metrics and constraints, such as service latency, network capacity, backhaul rate, and energy efficiency [7]. The content caching and on-off switch of BSs are considered together to minimize energy consumption in [8], in which the joint content caching and activation is formulated as an NP-hard problem and solved with a novel approximation framework. Wang et al. .......
陈述作者的研究基础。
Unlike the related works, this paper uses the actor–critic RL framework to formulate the joint optimization problem in the fog-enabled IoT, and proposes the Natural policy-gradientbased deep RL algorithm to learn the optimal stochastic policy. The contributions of this paper are as follows.
陈述该项研究的成果以及该项研究在不同领域中的作用。
研究成果:
1) We provide a joint optimization solution for content caching, computing offloading, and radio resources allocation in the fog-enabled IoT with the objective of minimizing the average end-to-end delay for all service requests
2) We use the model-free RL framework to optimize the policy by interacting with the
......
4) We utilize two up-to-date techniques, namely fixed target network and experience replay, in order to avoid the divergence of the deep RL algorithm and increase stability. Furthermore, the Natural policy gradient method is used to avoid converging to the local maximum.
概述当前研究目的(可省略)
介绍全文的篇章结构
The rest of this paper is organized as follows. Section II presents the review of the related studies and the new progress of deep RL. Section III elaborates the system models and definitions used in this paper. Section IV formulates the joint optimization problem as a model-free RL problem under unknown stochastic environment. Section V proposes the actor–critic deep RL algorithm to learn the optimal parameterized policy. The evaluation results are examined in Section VI and we conclude this paper in Section VII.
原文链接:https://blog.csdn.net/u011650143/article/details/54428069
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