摘 要
随着物联网技术的快速发展，海量设备产生的数据对传统云计算架构提出了严峻挑战，尤其是在实时性、带宽消耗和隐私保护等方面。为解决这一问题，本研究聚焦于基于边缘计算的物联网数据处理优化方法，旨在通过将计算任务从云端迁移至靠近数据源的边缘节点，提升系统性能并降低资源开销。为此，本文提出了一种融合任务卸载策略与资源分配机制的综合优化框架，该框架能够根据任务特性及网络状态动态调整计算资源分配方案。具体而言，研究设计了一种基于深度强化学习的任务调度算法，以实现任务执行时延与能耗之间的平衡，并结合边缘节点的存储与计算能力，构建了高效的本地化数据处理模型。实验结果表明，所提方法在多种典型物联网应用场景中显著降低了平均任务响应时间，同时有效减少了云端传输负载。此外，通过引入差分隐私技术，进一步增强了用户数据的安全性和隐私保护水平。本研究的主要贡献在于提出了一种适应性强且高效的数据处理优化策略，不仅提升了边缘计算在物联网环境中的适用性，还为未来智能边缘系统的开发提供了理论支持和技术参考。
关键词：物联网数据处理；边缘计算优化；任务卸载策略；深度强化学习；差分隐私保护
Research on Optimization of IoT Data Processing Based on Edge Computing
Abstract: With the rapid development of Internet of Things (IoT) technology, the massive data generated by a large number of devices pose significant challenges to traditional cloud computing architectures, particularly in terms of real-time performance, bandwidth consumption, and privacy protection. To address these issues, this study focuses on optimizing IoT data processing based on edge computing, aiming to improve system performance and reduce resource overhead by migrating computational tasks from the cloud to edge nodes closer to the data source. Specifically, a comprehensive optimization fr amework integrating task offloading strategies and resource allocation mechanisms is proposed, which dynamically adjusts computational resource allocation schemes according to task characteristics and network conditions. A deep reinforcement learning-based task scheduling algorithm is designed to balance execution delay and energy consumption while leveraging the storage and computational capabilities of edge nodes to construct an efficient localized data processing model. Experimental results demonstrate that the proposed method significantly reduces average task response time across various typical IoT application scenarios and effectively alleviates the transmission load on the cloud. Furthermore, by incorporating differential privacy techniques, the security and privacy protection level of user data are enhanced. The primary contribution of this research lies in proposing a robust and efficient data processing optimization strategy that not only enhances the applicability of edge computing in IoT environments but also provides theoretical support and technical references for the development of future intelligent edge systems.
Keywords: Internet Of Things Data Processing; Edge Computing Optimization; Task Offloading Strategy; Deep Reinforcement Learning; Differential Privacy Protection
目 录
1绪论 1
1.1边缘计算与物联网数据处理的背景 1
1.2研究基于边缘计算的数据处理优化的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2边缘计算架构下的数据处理需求分析 2
2.1物联网数据的特点与挑战 2
2.2边缘计算对数据处理的需求 3
2.3数据处理优化的关键问题 3
2.4典型应用场景中的需求剖析 4
2.5需求驱动的优化目标设定 4
3基于边缘计算的数据处理优化策略 5
3.1数据分流与任务卸载优化 5
3.2资源分配与调度机制设计 5
3.3数据压缩与传输效率提升 6
3.4实时性与能耗平衡策略 6
3.5异构设备协同处理框架 7
4优化方案的实现与性能评估 7
4.1优化算法的设计与实现 7
4.2测试环境与实验平台搭建 8
4.3性能指标体系的构建 8
4.4实验结果分析与讨论 9
4.5方案的优势与局限性 9
结论 10
参考文献 11
致 谢 12
