scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

scholarly journals A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges

2022 ◽  
Author(s):  
Jithin Jagannath ◽  
Anu Jagannath ◽  
Prem Sagar Pattanshetty Vasanth Kumar
Keyword(s):  
Deep Learning ◽  
Radio Frequency ◽  
Communication Networks ◽  
Data Privacy ◽  
Autonomous Driving ◽  
Security Measures ◽  
Comprehensive Survey ◽  
Massive Scale ◽  
Rf Fingerprinting ◽  
Iot Devices

Fifth generation (5G) networks and beyond envisions massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL) based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect - background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues - necessary to elucidate this topic to the reader in an encyclopedic manner.

DEEP LEARNING WITH LONG-TERM SHORT-TERM MEMORY FOR IOT TRAFFIC PREDICTION

2021 ◽  
Author(s):  
А.С. БОРОДИН ◽  
А.Р. АБДЕЛЛАХ ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Deep Learning ◽  
Communication Networks ◽  
Short Term Memory ◽  
Traffic Prediction ◽  
Short Term ◽  
Term Memory ◽  
Traffic Forecasting ◽  
Long Short Term Memory ◽  
Iot Devices

Использование искусственного интеллекта в сетях связи пятого (5G) и последующих поколений дает новые возможности, в том числе для прогнозирования трафика. Это особенно важно для трафика интернета вещей (IoT - Internet of Things), поскольку число устройств IoT очень велико. Предлагается для прогнозирования трафика IoT применить глубокое обучение с использованием нейронной сети долговременной краткосрочной памяти LSTM (Long Short-Term Memory). The use of artificial intelligence in communication networks of the 5G and subsequent generations provides completely new opportunities, including for traffic forecasting. This is especially important for IoT traffic because the number of IoT devices is very large. The article proposes to apply deep learning to predict IoT traffic using a neural network of longterm short-term memory (LSTM).

scholarly journals Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
M. Rubaiyat Hossain Mondal ◽  
Subrato Bharati ◽  
Prajoy Podder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Privacy ◽  
Meta Analysis ◽  
Future Research ◽  
Research Papers ◽  
Convolutional Network ◽  
X Ray ◽  
Comprehensive Survey ◽  
Novel Coronavirus

Background: This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & Method: The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results: Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. Conclusion: Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.

scholarly journals A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 471
Author(s):  
Zhiyang Guo ◽  
Yingping Huang ◽  
Xing Hu ◽  
Hongjian Wei ◽  
Baigan Zhao
Keyword(s):  
Deep Learning ◽  
Evaluation Criteria ◽  
Scene Understanding ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Performance Comparison ◽  
Advantages And Disadvantages ◽  
Future Challenges ◽  
Benchmark Datasets ◽  
Comprehensive Survey

As a prerequisite for autonomous driving, scene understanding has attracted extensive research. With the rise of the convolutional neural network (CNN)-based deep learning technique, research on scene understanding has achieved significant progress. This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving. We categorize these works into four work streams, including object detection, full scene semantic segmentation, instance segmentation, and lane line segmentation. We discuss and analyze these works according to their characteristics, advantages and disadvantages, and basic frameworks. We also summarize the benchmark datasets and evaluation criteria used in the research community and make a performance comparison of some of the latest works. Lastly, we summarize the review work and provide a discussion on the future challenges of the research domain.

Bringing Security to The Smallest Embedded Systems

2017 ◽  
Author(s):  
JOSEPH YIU
Keyword(s):  
Internet Of Things ◽  
Embedded Systems ◽  
Low Cost ◽  
Security Requirements ◽  
The Internet ◽  
Security Measures ◽  
Significant Challenge ◽  
Iot Devices

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:

scholarly journals An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6885
Author(s):  
Sahar Ujan ◽  
Neda Navidi ◽  
Rene Jr Landry
Keyword(s):  
Feature Extraction ◽  
Radio Frequency ◽  
Transfer Learning ◽  
Communication Networks ◽  
Continuous Wave ◽  
Critical Role ◽  
Video Stream ◽  
Radio Frequency Interference ◽  
Wave Interference ◽  
Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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