基于信任模型的分簇WSNs可靠数据采集方法

A Reliable Data Gathering Algorithm for Clustering WSNs Based on Trust Model

针对无线传感器网络由于节点异常或受干扰等原因导致网络监测数据可靠性降低的问题,提出一种基于信任模型的可靠数据采集方法。该方法首先利用箱型模型筛选当前时刻采集的数据值变化较大的节点,并判断其是否为异常数据节点;然后,根据异常数据节点的占比和节点可信度确定数据是否被采集。当数据异常节点数量低于设定阈值时,基于信任模型过滤掉可信度低的非正常节点后再对数据进行采集;当高于设定阈值时,启动监测事件发生时的紧急数据传输机制,以保障紧急数据的快速收集。仿真实验结果表明:该算法在不同异常节点占比下较经典信任模型RFSN平均检测率高9.14%左右;较原生HEED协议平均数据采集准确率高20%左右。

Aiming at the problem that the reliability of monitoring data becomes low due to abnormal or interfered nodes in wireless sensor networks(WSNs),a data gathering algorithm based on trust model is proposed.The algorithm firstly uses the box model to select the nodes whose data values change greatly at the current moment and determines whether they are abnormal data nodes.Then,whether data is collected is determined according to the proportion of abnormal data nodes and the credibility of nodes.When the number of abnormal nodes is lower than the set threshold,data is collected after filtering out the abnormal nodes with low credibility based on the trust model;when is higher than the set threshold,the urgent data transport mechanism for the occurrence of monitoring events is activated to ensure the rapid data collection.The simulation results show that the average detection rate of the proposed algorithm is about 9.14%higher than that of the classical trust model RFSN under different proportions of abnormal nodes,and the average data gathering accuracy is about 20%higher than that of the original HEED protocol.