Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network's performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols' performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.
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http://dx.doi.org/10.3390/s21041179 | DOI Listing |
Sensors (Basel)
December 2024
Stony Brook Institute at Anhui University, Hefei 230000, China.
The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure.
View Article and Find Full Text PDFSensors (Basel)
December 2024
LASSENA-Laboratory of Space Technologies, Embedded Systems, Navigation and Avionics, École de Technologie Supérieure (ETS), Montreal, QC H3C-1K3, Canada.
The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver's post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Institute of Communications and Navigation, German Aerospace Center (DLR), 51147 Köln, Germany.
Spoofing attacks pose a significant security risk for organizations and systems relying on global navigation satellite systems (GNSS) for their operations. While the existing spoofing detection methods have shown some effectiveness, these can be vulnerable to certain attacks, such as secure code estimation and replay (SCER) attacks, among others.This paper analyzes the potential of satellite fingerprinting methods for GNSS spoofing detection and benchmarks their performance using real (in realistic scenarios by using GPS and Galileo signals generated and recorded in the advanced GNSS simulation facility of DLR) GNSS signals and scenarios.
View Article and Find Full Text PDFSensors (Basel)
November 2024
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Modern radar technology requires high-quality signals and detection performance. However, traditional frequency-modulated continuous wave (FMCW) radar often has poor anti-jamming capabilities, and the high sampling rates associated with large time-bandwidth product signals can lead to increased system hardware costs and reduced data processing efficiency. This paper constructed a composite radar waveform based on noise frequency modulation (NFM) and linear frequency modulation (LFM) signals, enhancing the signal's complexity and anti-jamming capability.
View Article and Find Full Text PDFCurr Biol
December 2024
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD 21218, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA; The Solomon Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Echolocating bats rely on rapid processing of auditory information to guide moment-to-moment decisions related to echolocation call design and flight path selection. The fidelity of sonar echoes, however, can be disrupted in natural settings due to occlusions, noise, and conspecific jamming signals. Behavioral sensorimotor adaptation to external blocks of relevant cues has been studied extensively, but little is known about adaptations that mitigate internal sensory flow interruption.
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