摘 要
无线传感器网络（Wireless Sensor Networks, WSNs）作为物联网技术的重要组成部分，在环境监测、智能交通和医疗健康等领域展现出广阔的应用前景。然而，由于节点资源受限及复杂多变的通信环境，如何高效处理和传输感知数据成为亟待解决的关键问题。本研究以提升WSNs的数据处理效率和传输性能为目标，深入探讨了数据融合与传输技术的优化方法。通过引入分层式数据融合模型，结合分布式压缩感知理论，提出了一种基于节点协作的自适应数据融合算法，有效降低了冗余数据量并提升了信息提取精度。同时，针对传统路由协议在高负载场景下的能耗不均衡问题，设计了一种能量感知的动态路由选择机制，该机制综合考虑节点剩余能量和链路质量，显著延长了网络生命周期。实验结果表明，所提出的融合与传输方案能够在保证数据完整性的前提下，减少约30%的通信开销，并将网络生存时间提高40%以上。此外，本研究还创新性地将机器学习技术应用于数据预处理阶段，进一步增强了对异常数据的识别能力，为WSNs的实际部署提供了可靠的技术支持。综上所述，本研究不仅为数据融合与传输技术的发展奠定了理论基础，也为未来智能化WSN系统的设计提供了重要参考。
关键词：无线传感器网络；数据融合；传输优化；能量感知路由；机器学习
Abstract
Wireless Sensor Networks (WSNs), as a crucial component of Internet of Things (IoT) technology, demonstrate extensive application potential in areas such as environmental monitoring, intelligent transportation, and healthcare. However, due to the limited resources of nodes and the complex, dynamic communication environment, how to efficiently process and transmit sensory data has become a critical issue that needs to be addressed. This study aims to enhance the data processing efficiency and transmission performance of WSNs by thoroughly investigating optimization methods for data fusion and transmission technologies. By introducing a hierarchical data fusion model integrated with distributed compressive sensing theory, an adaptive data fusion algorithm based on node collaboration is proposed, which effectively reduces redundant data volume and improves the accuracy of information extraction. Additionally, in response to the energy consumption imbalance issue of traditional routing protocols under high-load scenarios, an energy-aware dynamic routing selection mechanism is designed. This mechanism comprehensively considers the residual energy of nodes and link quality, significantly extending the network lifetime. Experimental results indicate that the proposed fusion and transmission scheme can reduce communication overhead by approximately 30% while ensuring data integrity and increase network survival time by over 40%. Furthermore, this research innovatively applies machine learning techniques to the data preprocessing stage, thereby enhancing the ability to identify abnormal data and providing reliable technical support for the practical deployment of WSNs. In summary, this study not only establishes a theoretical foundation for the development of data fusion and transmission technologies but also provides significant references for the design of future intelligent WSN systems.
Keywords: Wireless Sensor Network; Data Fusion; Transmission Optimization; Energy-Aware Routing; Machine Learning
目 录
1绪论 1
1.1无线传感器网络的发展背景与意义 1
1.2数据融合与传输技术的研究现状 1
1.3研究方法与技术路线 2
2数据融合技术的理论基础 2
2.1数据融合的基本概念与分类 3
2.2融合算法在无线传感器网络中的应用 3
2.3数据冗余与信息压缩技术分析 4
2.4节点协作机制对数据融合的影响 4
3数据传输技术的关键问题 5
3.1无线传感器网络中的通信协议研究 5
3.2能量优化的传输策略设计 5
3.3延迟与带宽约束下的传输性能分析 6
3.4安全性与可靠性保障机制 6
4数据融合与传输的协同优化 7
4.1融合与传输的交互关系分析 7
4.2基于多目标优化的系统设计 7
4.3实时性要求下的资源分配策略 8
4.4案例分析与实验验证 9
结论 9
参考文献 11
致 谢 12
