Interference Characterization in Cellular-Assisted Vehicular Communications With Jamming

Arif, Mohammad; Kim, Wooseong; Qureshi, Sadia

doi:10.1109/access.2022.3168709

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…Moreover, the standardization of communication protocols and security measures across different EV technologies and charging infrastructures remains a challenge [16]. Future research should focus on developing universal security standards and protocols that can be applied across various platforms and technologies, ensuring a cohesive and secure EV charging ecosystem.…”

Section: Future Directions and Research Opportunitiesmentioning

confidence: 99%

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Kilichev,

Turimov,

Kim

2024

Mathematics

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In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

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Section: Future Directions and Research Opportunitiesmentioning

confidence: 99%

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Kilichev,

Turimov,

Kim

2024

Mathematics

Self Cite

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show abstract

“…The performance of a user device in cellular-assisted vehicular communications is characterized in [14]. The authors modeled cellular base stations using PPP and improved the networks' coverage as well as spectrum efficiency performance in the presence of vehicular, cellular, and jamming interference.…”

Section: Applications Of Ppp In Vehicular Communicationsmentioning

confidence: 99%

Analysis of Fluctuating Antenna Beamwidth in UAV-Assisted Cellular Networks

Arif,

Kim

2023

Mathematics

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This paper investigates a cellular network assisted by unmanned aerial vehicles (UAVs) in the presence of a fluctuating 3-dimensional (3D) antenna beamwidth. The primary objective is to perform an analysis of typical user equipment (T-UE) performance with a specific focus on coverage probability and spectral efficiency (SE) in the presence of fluctuations of 3D antenna beamwidth. Within this analytical framework, the macro base stations (MBSs) are meticulously characterized through the application of an independent 2D homogeneous Poisson point process (PPP), while the low-altitude platforms (LAPs) are described using an independent 3D PPP. The study entails the derivation of association probabilities, determining the likelihood of the T-UE associating with MBSs, line-of-sight LAPs, and non-line-of-sight LAPs. Through rigorous mathematical analysis, the paper formulates precise analytical expressions that encapsulate the association and coverage probabilities, taking into account the inherent variability in the UAV antenna beamwidth. This research focuses on a thorough performance evaluation of the T-UE across diverse network configurations, encompassing LAP density, the transmission power of LAPs, and the critical signal-to-interference ratio threshold. The outcomes of this study distinctly underscore the substantial disruptive impact resulting from fluctuating beamwidth on the performance of the T-UE within the UAV-assisted cellular network. Additionally, this performance is further impacted by larger densities and transmission power of the LAP. Hence, it is imperative to take into account the influence of these fluctuations on network association, coverage, and SE whenever contemplating a UAV-assisted cellular network.

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Decision making for selection of smart vehicle transportation system using VIKOR approach

Khan,

Ali,

Sohail

et al. 2024

Int J Data Sci Anal

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Interference Characterization in Cellular-Assisted Vehicular Communications With Jamming

Cited by 10 publications

References 35 publications

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Analysis of Fluctuating Antenna Beamwidth in UAV-Assisted Cellular Networks

Decision making for selection of smart vehicle transportation system using VIKOR approach

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