Physical Layer Security of Cognitive Ambient Backscatter Communications for Green Internet-of-Things

Li, Xingwang; Zheng, Yike; Khan, Wali Ullah; Zeng, Ming; Liu, Dong; Ragesh, G. K.; Li, Lihua

doi:10.1109/tgcn.2021.3062060

Cited by 110 publications

(41 citation statements)

References 47 publications

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“…erefore, this technology has become the focus of any artificial intelligence company, and it is also very necessary for the governments of many countries/regions in the world [18]. In addition, researchers are also focusing on the modernization of other scientific fields such as artificial intelligence and information technology [19][20][21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Ahmad¹,

Ullah

Khan

et al. 2021

Journal of Healthcare Engineering

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Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

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Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Ahmad¹,

Ullah

Khan

et al. 2021

Journal of Healthcare Engineering

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“…The work [21] investigated DCT-I based LS and LMMSE estimators for CE applications in LTE uplink systems, but the high computational complexity makes such estimators unsuitable for uplink NB-IoT systems. Besides the works on NB-IoT, researchers have also studied other emerging technologies such as non-orthogonal multiple access , backscatter communication, vehicle to everything, cognitive radio, blockchain, artificial intelligence, and physical layer security [46]- [51].…”

Section: Related Workmentioning

confidence: 99%

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

et al. 2021

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Channel estimation is a challenging and timely issue for the 3rd Generation Partnership Project (3GPP) standardized low power wide-area network technology named narrowband Internet of Things (NB-IoT). Channel estimation is crucial to achieve extended radio coverage, energy efficiency, coherent detection, and channel equalization for signal repetition dominated NB-IoT uplink transmission. The NB-IoT inherits simplified baseband radio frequency processing, physical channels, reference signal structure, and numerology from existing Long Term Evolution (LTE) systems to save power and costs. Thus, channel estimation methods extensively employed in LTE systems may not be applied to the NB-IoT uplink systems. In this paper, efficient discrete cosine transform type-I (DCT-I)-based transform-domain channel estimation approaches are proposed by modifying the original definition of DCT-I. The proposed methods can mitigate the problems experienced in the discrete Fourier transform (DFT)-based channel estimation, such as signal aliasing error, and border effect. The proposed approaches improve channel estimation precision by reducing signal distortion from the high-frequency region in the time-domain when non-sample-spaced path delays exist in multipath fading channels. Signal aliasing error experienced from the virtual subcarriers is also minimized with the anticipated schemes. The proposed methods are applied on simple least squares (LS) estimates in time-domain to eliminate estimation noise. The viability of the proposed estimators is verified as compared to the conventional LS, DFT-based de-noising LS, and standard DCT-I based methods through extensive numerical simulations. Based on the numerical simulations, the proposed estimators show better mean square error and bit error rate performances than their competitors in extremely low coverage conditions. INDEX TERMSChannel estimation, Internet of Things (IoT), Long Term Evolution (LTE), narrowband Internet of Things (NB-IoT), orthogonal frequency-division multiplexing (OFDM), signal repetition I. INTRODUCTION N ARROWBAND Internet of Things (NB-IoT) is an emerging and extremely narrowband cellular-based low power wide-area network (LPWAN) technology [1]-[5], which was introduced by the 3rd Generation Partnership Project (3GPP) as a representative technology in release-13 for providing massive connectivity of geographically distributed IoT devices to the Internet. The NB-IoT will be a strategic technology in the 5th generation (5G) new radio networks, and a crucial driver in future semiconductor

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“…Besides health issues, researchers in academia and industry also investigated other problems in science, technology, and engineering using various DL-based approaches [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

Tufail

Ullah

Khan

et al. 2021

Wireless Communications and Mobile Computing

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Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data.

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Physical Layer Security of Cognitive Ambient Backscatter Communications for Green Internet-of-Things

Cited by 110 publications

References 47 publications

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

On Efficient DCT Type-I Based Low Complexity Channel Estimation for Uplink NB-IoT Systems

Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

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