Abstract
Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.
Highlights
Wireless sensor networks (WSNs) are utilized in different fields including healthcare monitoring, industrials, military, air pollution, water quality monitoring, security monitoring, wearable devices, internet of things, and more [1] [2]
We propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment
The Centroid Localization (CL) is utilized to evaluate the effect of jammed nodes when we estimate jammer location with different scenario compared to KF
Summary
Wireless sensor networks (WSNs) are utilized in different fields including healthcare monitoring, industrials, military, air pollution, water quality monitoring, security monitoring, wearable devices, internet of things, and more [1] [2]. A jamming attack may block the sensors from communicating with their neighbor by emitting its signal with high power to prevent a legitimate node from transmitting its data [3]. A random jammer transmits the constant random data to its target This type of attack is a time domain because the jammer sends its jamming signal periodically and switches to sleep mode. Several algorithms have been proposed as anti-jamming attacks in wireless communication, such as the Frequency Hopping Spread Spectrum (FHSS) and the Direct Sequence Spread Spectrum (DSSS) [7] Both FHSS and DSSS are based on a secret shared key between nodes or sensors before exchanging their information [8].
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