基于节点感知信任度模型的无线传感网络事件检测方法

事件区域检测是无线传感器网络在复杂环境感知中的一项重要应用,然而节点受到环境中多种不确定性因素(如温度、湿度、硬件条件等)的影响导致其感知数据出现错误,降低了节点对事件检测的准确度. 根据邻近节点之间的感知信息存在一定的空间相关性,提出一种基于网络拓扑的节点感知信任度模型,在此基础上每个节点将自身感知信息与邻居节点交换信息相结合,建立贝叶斯事件检测容错模型,计算事件发生的预测概率,同时确定事件发生的最优阈值,节点根据概率预测结果与最优阈值相比较对事件发生做最终决策. 仿真结果表明,与典型的贝叶斯最优阈值容错算法相比,在节点故障率为25%的情况下,所提算法可将错误节点修正率由65%提高到78%;而在节点故障率为30%的情况下,所提算法可将错误节点修正率由50%提高到70%,体现出了良好的容错性能.

Event Detection Method Based on Belief Model for Wireless Sensor Networks

Event area detection is one of the most important applications in complex environment for wire-less sensor networks. The nodes in the networks may have faults due to many uncertainty factors ( such as temperature, humidity, hardware etc. ) , which will reduce event detection quality. Considering the spa-tial correlation for the sensing information of neighboring sensors, a belief model based on network topolo-gy was proposed. A Bayesian fault tolerance model was further constructed by synthesizing sensor’ s own information and its neighbor nodes’ information. The model can predict the probability as well as the op-timum threshold on whether the event happens. The node makes the final decisions by comparing the two probability values. Simulations show that compared with the optimal threshold decision scheme, the pro-posed scheme can increase the percentage of the corrected nodes from 65% to 78% when node fault prob-ability is 25%. The scheme can also increase the percentage of the corrected nodes from 50% to 70%when the node fault probability is 30% that reflects better fault-tolerance performance.