期刊文献+

基于云计算技术的无线传感通信链路优化策略研究

Research on Optimization Strategies for Wireless Sensor Communication Links Based on Cloud Computing Technology
下载PDF
导出
摘要 随着物联网技术的迅速发展,无线传感器网络(Wireless Sensor Network,WSN)在各类应用中的普及程度日益提高。然而,WSN存在数据传输效率低下、能耗高、安全性不足等问题,成为制约其进一步发展的瓶颈。云计算具有强大的数据处理和存储能力,是解决上述问题的关键。文章概述云计算技术,提出一种基于云计算技术的无线传感通信链路优化策略。该策略通过计算节点间的综合信任值,筛除恶意数据,并对数据进行云压缩,以减轻网络通信负担。为验证这一优化策略的实际效果,设计相关实验。实验结果表明,该策略显著提升了WSN的数据传输效率、安全性及可靠性。 With the rapid development of Internet of Things technology,Wireless Sensor Networks(WSN)are becoming more and more popular in various applications.However,there are some problems in wireless sensor networks,such as low efficiency of data transmission,high energy consumption and insufficient security,which restrict its further development.Cloud computing,with its powerful data processing and storage capabilities,is the key to solving these problems.In this paper,the cloud computing technology is summarized,and a wireless sensor communication link optimization strategy based on cloud computing technology is proposed.The strategy can reduce the network communication burden by computing the integrated trust degree between nodes,filtering the malicious data and compressing the data in the cloud.In order to verify the actual effect of this optimization strategy,some experiments were designed.Experimental results show that the proposed strategy significantly improves the efficiency,security and reliability of data transmission in wireless sensor networks.
作者 徐战威 XU Zhanwei(Henan Industrial and Trade Vocational College,Zhengzhou 451191,China)
出处 《通信电源技术》 2024年第8期185-187,共3页 Telecom Power Technology
关键词 无线传感器网络(WSN) 云计算技术 综合信任值 Wireless Sensor Networks(WSN) cloud computing technology integrated trust degree
  • 相关文献

参考文献6

二级参考文献58

  • 1王灵矫,方凯鹏,郭华.改进的粒子群蒙特卡洛WSN节点定位算法[J].计算机科学,2022,49(S02):882-886. 被引量:6
  • 2Hubert P. Applications of time series analysis in astronomy and meteorology [M]. London Chapman and Hall, 1997.
  • 3Hubert P. The segmentation procedure as a tool for discrete modeling of hydrometeorogieal regimes [J]. Stochastic Envi- ronment Resource Risk Assessment, 2000, 14: 297-304.
  • 4Kehagias A. A hidden Markov model segmentation procedure for hydrological and environmental time series [ J ]. Stochastic Environment Resource Risk Asesment, 2004, 18. 117-130.
  • 5Keogh E. An online algorithm for segmenting time series [C] // IEEE Computer Society. Proceedings of the 2001 IEEE International Conference on Data Mining, Washington, DC, 2001: 289-296.
  • 6Keogh E, Chu S, Hart D. Data mining in time series database [M]. Singapore: World Scientific Publishing Company, 2003.
  • 7Keogh E, Kasetty S. On the need for time series data mining benchmarks., a survey and empirical demonstration [C] // 8ta ACM International Conference on Knowledge Discovery and Data Mining, Edmonton, 2002.
  • 8Keogh E, Lonardi S, Ratanamahatana C. Towards parameter- free data mining [C] //10th ACM International Conference on Knowledge Discovery and Data Mining, Seattle, 2004.
  • 9Fortin V, Perreault L, Salas J D. Retrospective analysis and forecasting of streamflows using a shifting level model [J]. Journal of Hydrometeorol, 2004, 296 (1-4): 135-163.
  • 10Chen H L. Testing hydrologic time series for stationary [J]. Journal of Hydrometeorol Engineer, 2002, 7 (2) : 129-136.

共引文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部