一种基于点割集的感应失效节点容错算法

感应器失效节点通常发送错误数据,干扰全局信息判断,若转为睡眠状态则容易造成网络连通度下降,增加其他节点的路由转发负载.因此,对这些感应器失效节点的剩余能量进行利用,并进行自身估值,对于获取更准确的全局信息,保持网络负载平衡,具有重要的意义.提出一种基于点割集的感应失效节点容错算法,该算法基于数据相关图,筛选出与失效节点具有强数据相关性的点割集,然后利用所监听到的点割子集的观测量,进行正交估算,获取失效节点的最小均方误差估值.理论分析和实验结果表明,所提出的容错算法能较准确地估计失效点观测盲区,获取较完整的全局信息,同时由于算法使网络内的失效节点可以继续工作,保证了已有的网络负载平衡,维持原有的网络连通度.

A Faulty Sensor Node Tolerance Algorithm Based on Cut Point Set

In many sensor network applications, sensor collects correlated measurements of a physical field, for example: temperature field in a greenhouse. However, due to nodes' inherent instability and the severe environment, sensors are prone to fail. The measurements of a faulty sensor node will incur confusions in global readings, while turning them into sleeping mode will degrade network connectivity and overload balance. Therefore, it is significant to exploit residual energy of those faulty sensor nodes so as to obtain accurate integrated readings as well as overload balance. In this paper, a cut-point set based faulty sensor node tolerance algorithm is proposed by introducing the concepts of spatial correlation model, strong correlation graph and cut-point set. The algorithm first finds out a cut-point set, which has strong spatial correlation with faulty sensor node. According to the observations of the cut-point set, the faulty sensor node is able to predict its missing sensor readings by using orthogonal intersection estimation method. Analytic results show that the algorithm not only can tolerate the faulty sensor node, but also accurately predicts miss-readings, and keeps network connectivity and overload balance. The results of miss-readings estimation, obtained from simulations and greenhouse monitoring experiments, show that the methodology presented can successfully predict the missing sensor readings.