基于混合蒙特卡罗算法的网络隐式节点监测方法研究
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摘要
为了提高动态分簇传感网络的节点转发能力,提出了一种基于混合蒙特卡罗算法的网络隐式节点监测方法.首先,采用分布式均衡控制方法进行网络节点优化设计,构建动态分簇传感网络的输出信道模型.其次,利用自适应链路转发协议进行网络的路由探测设计,构建动态分簇传感网络的隐式节点路由均衡控制模型,提取隐式节点输出信息的关联特征量.最后,利用混合蒙特卡罗算法进行特征检测和信息提取,根据网络隐式节点的特征规律性分布对网络隐式节点信息进行监测.仿真结果表明,本文方法对网络隐式节点具有较好的监测效果,对网络隐式节点监测的最高丢包率仅为0.05,显著低于蚁群算法(0.29)与PEAS算法(0.48).
In order to improve the node forwarding ability of dynamic clustering sensor network, a hybrid implicit node monitoring method based on hybrid Monte Carlo algorithm is proposed. Firstly, the distributed equalization control method is used to optimize the network nodes, and the output channel model of the dynamic clustering sensor network is constructed. Secondly, the adaptive link forwarding protocol is used to design the route detection of the network. The implicit node routing equalization control model of the dynamic clustering sensor network is constructed, and the associated feature quantity of the implicit node output information is extracted. Finally, the hybrid Monte Carlo algorithm is used for feature detection and information extraction, and the network implicit node information is monitored according to the characteristic regular distribution of the network implicit nodes. The simulation results show that the highest packet loss rate of the implicit node monitoring in this method is only 0.05, which is significantly lower than the ant colony algorithm(0.29) and PEAS algorithm(0.48), so it indicates that the proposed method has better monitoring effect of network implicit nodes.
In order to improve the node forwarding ability of dynamic clustering sensor network, a hybrid implicit node monitoring method based on hybrid Monte Carlo algorithm is proposed. Firstly, the distributed equalization control method is used to optimize the network nodes, and the output channel model of the dynamic clustering sensor network is constructed. Secondly, the adaptive link forwarding protocol is used to design the route detection of the network. The implicit node routing equalization control model of the dynamic clustering sensor network is constructed, and the associated feature quantity of the implicit node output information is extracted. Finally, the hybrid Monte Carlo algorithm is used for feature detection and information extraction, and the network implicit node information is monitored according to the characteristic regular distribution of the network implicit nodes. The simulation results show that the highest packet loss rate of the implicit node monitoring in this method is only 0.05, which is significantly lower than the ant colony algorithm(0.29) and PEAS algorithm(0.48), so it indicates that the proposed method has better monitoring effect of network implicit nodes.
引文
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