Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources

Chen, Yi; Hong, Wen; Wu, Jinsong; Song, Huanhuan; Xu, Aidong; Jiang, Yixin; Zhang, Tengyue; Wang, Zhen

doi:10.3390/s19081926

Cited by 20 publications

(13 citation statements)

References 35 publications

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“…where = [ (1) , (2) , … ( ) ] is the transform matrix and is a diagonal matrix with sorted eigenvalues ( 1 ≥ 2 … ≥ ≥ ). Our main goal is to obtain the optimal unitary matrix * through maximizing the MI which can be obtained using:…”

Section: The Proposed Svd-based Pre-coding Methodsmentioning

confidence: 99%

“…According to the PCA approach, the covariance matrix of the ideal channel can be decomposed as = H , where = [ (1) , (2) , … ( ) ] is the transform matrix and is a diagonal matrix with some sorted eigenvalues. After transformation, matrix is transformed into = [ (0) , (1) , … ( −1) ] by expanding the CSI estimates with their projections on = .…”

Section: Example 2: Evaluation Of the Performance Ofmentioning

confidence: 99%

“…Efficient secret key generation of physical layer and authentication schemes based on wireless channels are developing issues in physical layer security, especially in Orthogonal Frequency Division Multiplexing (OFDM)based communication systems. Therefore, the channel parameter has received a lot of attention in literature as the fundamental part of key construction techniques [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Singular Value Decomposition Based Pre-coding for Secret Key Extraction from Correlated Orthogonal Frequency Division Multiplexing Sub-channels

Aliabadian

Zahabi

Mobini

2020

IJE

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Secret key extraction is a crucial issue in physical layer security and a less complex and, at the same time, a more robust scheme for the next generation of 5G and beyond. Unlike previous works on this topic, in which Orthogonal Frequency Division Multiplexing (OFDM) sub-channels were considered to be independent, the effect of correlation between sub-channels on the secret key rate is addressed in this paper. As an assumption, a realistic model for dependency among sub-channels is considered. Benchmarked by simulation, the result shows that the key exchange rate may decline by up to 72% due to the correlation of sub-channels. A new approach for efficient key extraction is used in this study. To do this, a Singular Value Decomposition based (SVD-based) pre-coding is utilized to alleviate the subchannels correlation and the channel noise. The low computational complexity of our proposed approach makes it a promising candidate for developing secure and high-speed networks. Results obtained through simulation indicate that applying pre-coding on the measured correlated data resulted in a minimum gain of 9 dB. In addition, the result also depicts the advantage of SVD versus other pre-coding techniques, namely PCA, DCT, and WT.

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Section: The Proposed Svd-based Pre-coding Methodsmentioning

confidence: 99%

Section: Example 2: Evaluation Of the Performance Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Singular Value Decomposition Based Pre-coding for Secret Key Extraction from Correlated Orthogonal Frequency Division Multiplexing Sub-channels

Aliabadian

Zahabi

Mobini

2020

IJE

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“…Gebhardt et al [ 73 ] also employed unsupervised anomaly detection for document authentication. Chen et al [ 74 ] used clustering in physical layer data from edge computing systems for authentication IoT devices that can be very effective to detect intruder IoMT devices in telehealth.…”

Section: Machine Learning Models In Authentication Schemes Of Telehealthmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Fard

Gebali

Mamun

2022

Sensors

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Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

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“…To eliminate drawbacks of authentication based on cipher approaches that are impacted by the large expenditures and energy constraints of smart devices, authors in [139] proposed a clustering-based physical-layer authentication scheme (CPAS) for systems with asymmetric resources in the mobile edge computing (MEC) environment. To ensure two-way authentication among edge devices and terminals, CPAS as cross-layer secure authentication merge symmetric cipher and clustering with information related to the wireless channel.…”

Section: A Review Of Articles For Special Issue On Green Energy-ementioning

confidence: 99%

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

Lörincz

Capone

2019

Sensors

Self Cite

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Although information and communications technologies (ICTs) have the potential of enabling powerful social, economic and environmental benefits, ICT systems give a non-negligible contribution to world electricity consumption and carbon dioxide (CO2) footprint. This contribution will sustain since the increased demand for user′s connectivity and an explosion of traffic volumes necessitate continuous expansion of current ICTs services and deployment of new infrastructures and technologies which must ensure the expected user experiences and performance. In this paper, analyses of costs for the global annual energy consumption of telecommunication networks, estimation of ICT sector CO2 footprint contribution and predictions of energy consumption of all connected user-related devices and equipment in the period 2011–2030 are presented. Since presented estimations of network energy consumption trends for main communication sectors by 2030 shows that highest contribution to global energy consumption will come from wireless access networks and data centres (DCs), the rest of the paper analyses technologies and concepts which can contribute to the energy-efficiency improvements of these two sectors. More specifically, different paradigms for wireless access networks such as millimetre-wave communications, Long-Term Evolution in unlicensed spectrum, ultra-dense heterogeneous networks, device-to-device communications and massive multiple-input multiple-output communications have been analysed as possible technologies for improvement of wireless networks energy efficiency. Additionally, approaches related to the DC resource management, DCs power management, green DC monitoring and thermal management in DCs have been discussed as promising approaches to improvement of DC power usage efficiency. For each of analysed technologies, future research challenges and open issues have been summarised and discussed. Lastly, an overview of the accepted papers in the Special Issue dedicated to the green, energy-efficient and sustainable networks is presented.

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Clustering Based Physical-Layer Authentication in Edge Computing Systems with Asymmetric Resources

Cited by 20 publications

References 35 publications

Optimal Singular Value Decomposition Based Pre-coding for Secret Key Extraction from Correlated Orthogonal Frequency Division Multiplexing Sub-channels

Optimal Singular Value Decomposition Based Pre-coding for Secret Key Extraction from Correlated Orthogonal Frequency Division Multiplexing Sub-channels

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Greener, Energy-Efficient and Sustainable Networks: State-Of-The-Art and New Trends

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