物联网异常节点定位方法的改进

The improvement of abnormal node localization method for Internet of things

传统的物联网异常节点定位方法采用模糊边缘覆盖方法,以节点之间的互信息量作为信息素引导,当节点规模较大和干扰较强时,定位准确度不高,为此,提出一种基于自适应信息融合跟踪检测的物联网异常节点定位算法.首先构建物联网节点之间通信传输信道模型,采用载波调制方法进行信道特征参量估计.然后利用自适应信息融合跟踪检测算法进行节点异常特征提取和检测,实现异常节点信息融合和滤波,通过接收端进行连续检测,实现异常节点的自适应分辨和定位.仿真结果表明,该方法在对物联网中异常节点定位时,误差能快速收敛到零,具有较好的准确性和实时性.

Traditional networking abnormal node locatingus uses fuzzy edge covering method, with the mutual information between nodes as information element guide. When a node with a large scale and strong interference, positioning accuracy is not high. Thus, an abnormal node localization algorithm based on adaptive information fusion tracking algorithm is proposed. Firstly, the communication transmission channel model between physical network node is constructed, and characteristic parameters of channel estimation is performed using carrier modulation method.Then, an adaptive information fusion tracking algorithm is used to extract and detect abnormal nodes feature, and the abnormal node information fusion and filtering are realized. By continuous detection at the receiving end, the abnormal node adaptive identification and localization are achieved. The simulation results show that when the method is used to locate the abnormal nodes in the Internet of things, the error can converge to zero quickly, which has good accuracy and real-time performance.