摘要
在标准Z igBee协议中没有设计相关的数据融合规范,使其只能用在低数据冗余的应用场合。针对大规模网络,网络中的数据冗余度很大,网络中的数据冗余会引起节点频繁地争抢信道,网络时延增加甚至出现网络瘫痪。为了解决这个问题提出了两层数据融合方法:第一层设计了终端节点到路由节点之间的统计融合,第二层设计了路由节点到网关的神经网络数据融合。实验证明,数据融合有利于降低数据冗余、改善查询效率、降低能量消耗,也使Z igBee协议适合高数据冗余的应用场合。
Related specialization of data fusion is not considered in Standard ZigBee protocol and this limits its application to low data redundancy. Data redundancy of mess network is high, which will lead to frequent channel competition, time delay of network and network paralysis. Two-layer data confusion was proposed to solve this problem; the first layer is on statistical confusion between terminal nodes and router nodes, and the second layer is on artificial neural network between router nodes and gateway node. Experiments prove data confusion is helpful to reduce data redundancy, improve query effect and reduce energy loss, and make ZigBee protocol suitable for high data redundancy.
出处
《计算机应用》
CSCD
北大核心
2009年第7期1897-1900,共4页
journal of Computer Applications
基金
福建省科技厅资助项目(2007F5039)
关键词
ZIGBEE协议
数据融合
人工神经网络
智能温室
ZigBee protocol
data fusion
Artificial Neural Network （ANN）
intelligent greenhouse
