期刊文献+

基于动态优化因子的Zigbee协议优化仿真算法 被引量:4

Zigbee Protocol Optimization Simulation Algorithm Based onDynamic Optimization Factors
下载PDF
导出
摘要 研究Zigbee无线网络协议的优化。当前的无线传感网络中以Zigbee协议为基础,传递过程存在很大的盲目性,节点分布存在较大的随机性。目前协议中,节点最优评估通信方法大多针对静态无权网络拓扑结构,一旦随机性增大,网络拓扑结构发生松动,造成非最优节点参与通信,引起协议效率低下。为了避免上述缺陷,提出了一种基于动态优化因子的Zigbee协议优化仿真算法。应用动态优化因子方法,对网络节点进行搜索,为网络协议优化提供准确的数据基础。利用簇树拓扑方法,为网络节点分配地址,从而实现Zigbee协议优化。实验结果表明,应用改进算法进行Zigbbee协议优化处理,能够提高网络服务效率。 Research Zigbee wireless network protocol optimization.The paper put forward a Zigbee protocol optimization algorithm based on dynamic optimization factors.The algorithm can search the network nodes and provide accurate data base for optimizing network protocols.Cluster tree topology method was used to distribute addresses for network nodes,so as to realize the optimization of Zigbee protocol.The experimental results show that the algorithm using Zigbee protocol optimization processing can improve the efficiency of network services.
机构地区 湛江师范学院
出处 《计算机仿真》 CSCD 北大核心 2013年第7期272-275,共4页 Computer Simulation
基金 湛江师范学院自然科学研究青年课题(QL1114)
关键词 动态优化 协议优化 簇树拓扑 Dynamic optimization Protocol optimization Cluster tree topology
  • 相关文献

参考文献8

二级参考文献27

  • 1吴家洲 姚远 徐华中.公共路灯远程监控系统研究[J].微计算机应用,2002,23(2).
  • 2Zigbee Alliance. Zigbee Specification. http://www. zigbee. org,2005 - 05.
  • 3Suggestions for the improvement of the IEEE 802. 15.4 standard, http://www. Ieee. org, 2003 - 07.
  • 4J. D. Lee, K. Y. Nam, S. H. Jeong, S. B. Choi, H. S. Ryoo, D. K. Kim. Development of Zigbee based Street Light Control System. Power Systems Conference and Exposition. 2006 -6.
  • 5Qing Fang, Feng Zhao. Lightweight sensing and communication protocols for target enumeration and aggregation [ C ]. Proc 4th ACM MOBIHOC, 2003. 165 - 176.
  • 6Yan Guo, Bei Hua. Energy- based Target Numeration in wireless sensor networks [ C ]. Proceedings of 2007 International Workshop on Wireless Ah Hoc, Mesh and Sensor Networks (WAMSNet - 07 ) , Seoul, Korea, Dec. 6 - 8, 2007.
  • 7N Shrivastava, R Mudumbai, U Madhow, S Suri. Target tracking with binary proximity sensors: fundamental limits, minimal descriptions and algorithms [ C ]. ACM Sensys, November 1 - 3, 2006.
  • 8Feng Zhao, Jie Liu, Juan Liu. Collaborative signal and information processing: an information directed approach [ C ]. Proceedings of the IEEE, 2003,91 (8) :1199 - 1209.
  • 9J J Liu, J Reich, Feng Zhao. Collaborative in - network processing for target tracking [ J ]. Journal of applied signal processing, 2003, (4) : 378 -391.
  • 10Hanbiao Wang, Jeremy Elson, Lewis Girod. Target classification and localization in habitat monitoring [ C ]. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Pro- cessing ( ICASSP 2003 ), Hong Kong, China, 2003 - 04.

共引文献87

同被引文献34

引证文献4

二级引证文献19

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