Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Chen, Songlin; Wen, Hong; Wu, Jinsong; Xu, Aidong; Jiang, Yixin; Song, Huanhuan; Chen, Yi

doi:10.3390/s19163610

Cited by 32 publications

(12 citation statements)

References 33 publications

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“…In reference [38], a lightweight terminal identity authentication scheme is proposed. The authors use a method based on radio frequency fingerprint identification (RFFID) to authenticate terminal devices with limited computing capabilities in the Internet of Things.…”

Section: Rf Fingerprint Authenticationmentioning

confidence: 99%

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Tian

Lin

Guo

et al. 2020

Sensors

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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Section: Rf Fingerprint Authenticationmentioning

confidence: 99%

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Tian

Lin

Guo

et al. 2020

Sensors

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“…Article [147] analyses the radio frequency fingerprinting identification (RFFID) approach dedicated to ensuring authentications for a high number of energy-limited user terminals working in the MEC environment. The proposed scheme combines a two-layer model with the use of non-encryption RFFID for IoT terminals.…”

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

confidence: 99%

“…In the second layer, implementation of machine learning algorithms through collected learning features and generated decision models is done in the distant cloud, which improves the speed of authentication. Through extensive simulations performed for scenario based on IoT implementation, the gained results indicate that the approach proposed in [147] can achieve lower device energy depletion and better recognition rate than the traditional RFFID method based on wavelet features.…”

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

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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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“…The traditional centralized computing architecture based on a cloud centre [3][4][5] has been unable to meet the requirements of modern devices and applications for low latency, high efficiency, and low cost applications. In some special scenarios, such as smart healthcare [6,7], identity recognition [9], smart homes [10], all have high requirements on time and accuracy. Transferring data to cloud servers will raise latency, but running artificial intelligence algorithms such as machine learning and deep learning locally will bring an additional consumption of computing and power to the device.…”

Section: Introductionmentioning

confidence: 99%

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

Yang

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In smart applications, such as smart medical devices, in order to prevent privacy leaks, more data needs to be processed and trained locally or near the local end. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), a machine learning method based on a collaboration of edge computing devices. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, when a malicious service node is found, verifiable evidence can be generated to build a trust mechanism. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment. Keywords: Machine learning • Edge computing • Privacy-preserving • Mobile devices • Outsourced computingWe would like to thank the anonymous reviewers for their careful reading and useful comments.

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Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

Cited by 32 publications

References 33 publications

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

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

A Secure and Verifiable Outsourcing Scheme for Machine Learning Data

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