作　　者： ;

机构地区： 广东工业大学华立学院

出　　处： 《计算机技术与发展》 2021年第2期127-132,共6页

摘　　要： 由于动态数据的节点分布处于动态变化的状态,极具不稳定性,无法为无线传感网络实时提供可靠信息,需要进行数据重构处理,提升无线传感网数据稳定性,提出基于动态数据压缩的无线传感网数据重构模型。通过构建多维节点组网模型,分析节点间位置关系,并得到其多模状态重组结果,利用重组结果融合并调度无线传感数据。在融合结果中提取无线传感网络数据的梯度向量,为动态数据压缩提供基础处理数据,结合动态压缩方法,实现无线传感网数据重构。经仿真实验结果可知,基于动态数据压缩的无线传感网数据重构能够有效利用数据时空相关性,压缩动态数据,高效降低重构残差值,提升节点位置获取精度,数据离群概率大大降低,同时有效降低了无线传感网数据的重构时间开销。因此,该数据处理模型能够明显增加数据重构效率,降低网内数据通信开销。 Because the node distribution of dynamic data is in the state of dynamic change,which is extremely unstable and cannot provide reliable information for wireless sensor network in real time,it is necessary to carry out data reconstruction processing and improve the data stability of wireless sensor network.A data reconstruction model of wireless sensor network based on dynamic data compression is proposed.By constructing the multi-dimensional node networking model,the position relationship between nodes is analyzed,and the results of multi-mode state reorganization are obtained.The reorganization results are used to fuse and schedule the wireless sensing data.The gradient vector of wireless sensing network data is extracted from the fusion result,which provides basic processing data for dynamic data compression.By the simulation results,the data refactoring of wireless sensor network based on dynamic data compression can effectively utilize the spatio-temporal correlation of data and compress dynamic data,reducing the refactoring residual value efficiently,and improving the precision of node location acquisition,which greatly reduces the data outlier probability and effectively decreases the time overhead of data reconstruction of wireless sensor network.Therefore,the data processing model can obviously increase the data reconstruction efficiency and reduce the data communication overhead.

关 键 词： 动态数据压缩 无线传感网 残差分析 重构模型 梯度向量 特征挖掘

领　　域： [自动化与计算机技术—计算机应用技术] [自动化与计算机技术—计算机科学与技术]