基于自适应扩展卡尔曼滤波器的传感器网络安全跟踪
Secure Tracking in Sensor Networks using Adaptive Extended Kalman Filter
摘要：
传感器节点的位置信息已经成为无线传感器网络（WSN）中许多应用的重要组成部分。位置估计和目标跟踪的重要性使它们成为许多安全攻击的目标。各种方法都试图提供高精度的位置信息，而他们中的许多人都忽略了一个事实，即无线传感器网络可能会部署在恶劣的环境。在本文中，我们解决的问题，安全地跟踪一个移动节点（MN）已经注意到以前很少。提出了一种基于扩展卡尔曼滤波器（EKF）的移动节点安全跟踪算法，该算法能够在存在恶意信标节点的情况下，对移动节点进行高分辨率的跟踪。它在跟踪过程中过滤并识别恶意信标数据。所提出的方法大大优于以前提出的安全算法的检测率或MSE。基于不同网络设置的实验数据显示出良好的结果。
英文摘要：
Location information of sensor nodes has become an essential part of many applications in Wireless Sensor Networks (WSN). The importance of location estimation and object tracking has made them the target of many security attacks. Various methods have tried to provide location information with high accuracy, while lots of them have neglected the fact that WSNs may be deployed in hostile environments. In this paper, we address the problem of securely tracking a Mobile Node (MN) which has been noticed very little previously. A novel secure tracking algorithm is proposed based on Extended Kalman Filter (EKF) that is capable of tracking a Mobile Node (MN) with high resolution in the presence of compromised or colluding malicious beacon nodes. It filters out and identifies the malicious beacon data in the process of tracking. The proposed method considerably outperforms the previously proposed secure algorithms in terms of either detection rate or MSE. The experimental data based on different settings for the network has shown promising results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
