不确定性网络连续高斯协同局部聚类更新方法
发布时间：2018-12-11 11:27
【摘要】：为提高不确定性无线传感器网络(wireless sensor network,WSN)模型的危险边界局部演化特性感知精度,提出了一种基于局部聚类的不确定性WSN模型网络局部前沿协同更新算法。首先,给出基于高斯的WSN感知距离不确定性模型和速度不确定性模型,并给出封闭形式的考虑WSN节点有限处理能力和能量约束的连续贝叶斯局部前沿速度更新模型;其次,基于局部聚类更新算法对WSN网络主节点、列表、辅助列表进行更新,实现危险连续局部前沿的实时更新,实现复杂危险演变特征的分布式准确预测;最后,通过实验对比,所提方法对于传感器节点故障和通信链路故障具有强大的鲁棒性。
[Abstract]:In order to improve the perceptual accuracy of the local evolution characteristics of uncertain (wireless sensor network,WSN (Wireless Sensor Network) model, a local frontier collaborative updating algorithm based on local clustering for uncertain WSN model is proposed. Firstly, the WSN perceptual distance uncertainty model and velocity uncertainty model based on Gao Si are given, and the continuous Bayesian local frontier velocity updating model considering the limited processing capacity and energy constraints of WSN nodes is given. Secondly, the main node, list and auxiliary list of WSN network are updated based on the local clustering updating algorithm to realize the real-time updating of the continuous local frontier of danger and the accurate and distributed prediction of the complex risk evolution characteristics. Finally, the proposed method is robust to sensor node fault and communication link fault through experimental comparison.
【作者单位】： 宿州职业技术学院计算机信息系;淮北师范大学计算机学院;
【基金】：国家自然科学基金No.61102117 安徽高校自然科学研究重点项目No.KJ2016A782~~
【分类号】：TN929.5;TP212.9
，
本文编号：2372463
[Abstract]:In order to improve the perceptual accuracy of the local evolution characteristics of uncertain (wireless sensor network,WSN (Wireless Sensor Network) model, a local frontier collaborative updating algorithm based on local clustering for uncertain WSN model is proposed. Firstly, the WSN perceptual distance uncertainty model and velocity uncertainty model based on Gao Si are given, and the continuous Bayesian local frontier velocity updating model considering the limited processing capacity and energy constraints of WSN nodes is given. Secondly, the main node, list and auxiliary list of WSN network are updated based on the local clustering updating algorithm to realize the real-time updating of the continuous local frontier of danger and the accurate and distributed prediction of the complex risk evolution characteristics. Finally, the proposed method is robust to sensor node fault and communication link fault through experimental comparison.
【作者单位】： 宿州职业技术学院计算机信息系;淮北师范大学计算机学院;
【基金】：国家自然科学基金No.61102117 安徽高校自然科学研究重点项目No.KJ2016A782~~
【分类号】：TN929.5;TP212.9
，
本文编号：2372463
本文链接：https://www.wllwen.com/kejilunwen/zidonghuakongzhilunwen/2372463.html
