利用扩展卡尔曼滤波跟踪无线传感器网络中的移动干扰机

Waleed Aldosari, M. Zohdy, Richard Olawoyin
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引用次数: 2

摘要

无线传感器网络由于采用共享无线介质,容易受到干扰攻击。干扰机可以根据其功能和策略干扰任何特定或整个无线电频率。确定干扰机的位置对于防止无线网络中的干扰和恢复通信信道具有十分重要的意义。为了支持现有的抗干扰技术,我们提出了一种基于扩展卡尔曼滤波(EKF)和接收功率的算法来跟踪干扰机。探测干扰机的位置是防御此类攻击的第一步。此外,估计干扰机的位置支持广泛的防御。本文采用基于接收功率的距离干扰机定位技术,设计了扩展卡尔曼滤波的位置、速度和加速度方法,检测外部恶意节点的位置。通过广泛的仿真来评估EKF与虚拟力迭代定位(VFIL)、加权质心定位(WCL)和质心定位算法(CL)的性能。与VFIL、WCL和CL相比,EKF被证明是高效率的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracking the Mobile Jammer in Wireless Sensor Networks Using Extended Kalman Filter
Wireless Sensor Networks (WSNs) are susceptible to jamming attacks due to the shared wireless medium. The jammer can disrupt any specific or entire radio frequency based on its function and strategies. Locating the jammer location is very important against the jamming in the wireless network and restore the communication channel. To support the existing anti-jamming techniques, we proposed an algorithm based on the Extended Kalman filter (EKF) and power received to track the jammer. Detecting jammer location is the first step taking to defend such attacks. Besides, estimating jammer location supports a wide range of defense. Range-based jammer localization technique based on the received power is used in this work to detect the external malicious node location by designed the position, velocity, and acceleration approach of Extended Kalman filter. An extensive simulation conducted to evaluate the performance of EKF compares to the Virtual Force Iteration Localization (VFIL), Weighted Centroid Localization (WCL), and Centroid Localization algorithms (CL). The EKF proves to be of high efficiency in comparison to VFIL, WCL, and CL.
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