摘 要：
物联网（IoT）的快速发展对网络架构提出了低延迟、高可靠性和高效资源利用的需求，然而传统网络架构在处理海量设备连接和实时数据传输时面临显著挑战。本研究旨在设计一种面向物联网的低延迟网络架构，并通过优化算法提升其性能。为此，首先分析了现有网络架构在延迟控制方面的不足，提出了一种基于边缘计算与软件定义网络（SDN）融合的新型架构模型。该模型通过将计算任务卸载至靠近数据源的边缘节点，有效减少了核心网络的负载和传输延迟。同时，引入了一种动态资源分配机制，结合深度强化学习算法实现对网络流量的智能调度与优化。实验结果表明，所提出的架构能够在多种物联网应用场景中显著降低端到端延迟，平均延迟较传统架构减少约40%，并在高并发场景下展现出更优的稳定性和扩展性。此外，该架构还具备灵活适配不同业务需求的能力，为未来物联网网络的设计提供了新的思路。本研究的主要创新点在于将边缘计算与SDN技术深度融合，并通过智能化优化方法解决了低延迟与高效率之间的权衡问题，为物联网领域的网络架构设计做出了重要贡献。
关键词：物联网；低延迟网络架构；边缘计算；软件定义网络；深度强化学习
Design and Optimization of Low-Latency Network Architecture for the Internet of Things
Abstract: The rapid development of the Internet of Things (IoT) has imposed demands on network architecture for low latency, high reliability, and efficient resource utilization; however, traditional network architectures face significant challenges in handling massive device connections and real-time data transmission. This study aims to design a low-latency network architecture tailored for IoT applications and enhance its performance through optimization algorithms. To achieve this, the inadequacies of existing network architectures in latency control were first analyzed, leading to the proposal of a novel architecture model that integrates edge computing with Software-Defined Networking (SDN). By offloading computational tasks to edge nodes closer to the data source, this model effectively reduces the load on the core network and minimizes transmission latency. Additionally, a dynamic resource allocation mechanism was introduced, leveraging deep reinforcement learning algorithms to enable intelligent scheduling and optimization of network traffic. Experimental results demonstrate that the proposed architecture significantly reduces end-to-end latency across various IoT application scenarios, achieving an average reduction of approximately 40% compared to traditional architectures, while exhibiting superior stability and scalability under high-concurrency conditions. Moreover, the architecture is capable of flexibly adapting to diverse service requirements, offering new insights into the design of future IoT networks. The primary innovation of this research lies in the deep integration of edge computing and SDN technologies, combined with intelligent optimization methods to address the trade-off between low latency and high efficiency, thereby making a substantial contribution to the design of network architectures in the IoT domain.
Keywords: Internet Of Things; Low Latency Network Architecture; Edge Computing; Software Defined Network; Deep Reinforcement Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2物联网低延迟需求分析 2
2.1物联网应用场景与延迟要求 2
2.2数据传输特性对延迟的影响 3
2.3关键性能指标的定义与评估 3
2.4延迟优化的技术挑战 4
3低延迟网络架构设计 4
3.1架构设计的基本原则 4
3.2边缘计算在架构中的作用 5
3.3网络分层模型的设计思路 5
3.4数据流管理与路径优化策略 6
3.5架构设计的可行性验证 6
4网络优化技术与实现 7
4.1基于AI的延迟预测模型 7
4.2资源分配与调度优化方法 7
4.3网络协议的改进与适配 8
4.4实验环境搭建与测试方案 8
4.5优化效果评估与分析 9
结论 9
参考文献 11
致 谢 12
