摘要
由于传感网络结构拓扑呈现非均匀分布特性,且网络中的信号传输呈现非规则稀疏特性,导致现有方法很难对其数据进行准确高效的收集采样。于是,提出了融合网络覆盖度的数据压缩采样方法。方法首先针对传感网络结构拓扑的不确定性,通过计算网络覆盖冗余度,对工作节点采取自整定策略,在确保监测区域全覆盖的同时,有效削除网络中采集的冗余数据。然后基于最佳覆盖度,根据网络传输最小跳数的限定,以及监测密度,对节点采取动态分簇处理。再利用不同节点彼此的空间联系,来削弱错误数据对采样的干扰。最后通过比较矩阵与分解操作,对错误数据进行修复填充,并从稀疏数据中提高压缩采样的精度。仿真结果表明,融合网络覆盖度的数据压缩采样方法能够有效应对数据的不确定性与稀疏性,提高数据重构的精度与效率,且具有良好的网络覆盖度。
Due to the non-uniform distribution of the sensor network topology and the irregular sparsity of the sig⁃nal transmission in the network,it is difficult for the existing methods to collect and sample the data accurately and efficiently.Therefore,a data compression sampling method based on network coverage is proposed.Firstly,aiming at the uncertainty of the sensor network topology,the network coverage redundancy was calculated and the self-tuning strategy was adopted for the working nodes to effectively remove the redundant data collected in the network,while en⁃suring the full coverage of the monitoring area.Then,based on the optimal coverage,according to the limit of the minimum hop number of network transmission and the monitoring density,the nodes were dynamically clustered.The spatial relationship between different nodes was used to reduce the interference of wrong data to sampling.Finally,by comparing matrix and decomposition operation,the error data were repaired and filled,and the precision of compres⁃sion sampling was improved from sparse data.The simulation results show that the data compression sampling method based on network coverage can effectively deal with the uncertainty and sparsity of data,improve the accuracy and ef⁃ficiency of data reconstruction,and has good network coverage.
作者
邓宁宁
陈孝如
DENG Ning-ning;CHEN Xiao-ru(South China Institute of Software Engineering,Guangzhou University,Guangzhou Guangdong 510990,China)
出处
《计算机仿真》
北大核心
2020年第9期323-327,共5页
Computer Simulation
基金
广东省自然科学类重点科研项目(NO.2017KQNCX273、NO.2018KQNCX393)。
关键词
传感网络
覆盖冗余度
稀疏数据
数据压缩采样
Sensor network
Coverage redundancy
Sparse data
Data compression sampling
