基于稀疏分布的空间节点资源循环迭代控制算法

为解决大规模稀疏型传感网络数据随节点数目急剧增大时导致网络堵塞的问题,提出了基于稀疏分布的空间节点资源循环迭代控制算法。该方法利用大规模稀疏网络节点在空间上的弱相关性,构建了一个表达联合稀疏关系的模型。通过通信特征做到自适应选择最优节点作为感知节点,针对稀松节点数量有限、无法传递海量信息的问题,采用循环迭代控制对稀疏网络节点数据进行压缩,以最大程度用有限节点获得最大信息量;再利用信号稀疏性特征重构节点数据。仿真结果表明,该方法以有限的节点资源满足估计精确度的要求,并有效减少了感知的节点数目,降低系统的资源消耗。

Circulation Iterative Control Algorithm for Space Node Resources Based on Sparse Distribution

To solve the large-scale sparse data type sensor network with node number increase sharply when lead to network congestion problem, was proposed based on the distribution of the sparse space node resources circulation iterative control algorithm. The method using large-scale sparse network node in space weak correlation, built a express joint sparse relationship model, through the communication characteristics as perception be adaptive to choose the optimal node, in view of the poor node number is limited, unable to deliver huge amounts of information, the loop iteration control on sparse network node data compression, to maximize the use limited node to obtain the largest amount of information, data reuse signal sparse feature reconstruction node. The simulation results show that this method takes the limited node resources to meet the requirements of the estimation accuracy, and reduce the number of nodes in perception, reduce the resource consumption of the system.