Due to the rapid development of the Internet of Things (IoT) applications, which generate vast volumes of data at high speeds, security and privacy issues have become challenging. IoT devices use the advanced encryption standard algorithm before transmission. However, since the system communicates through amplify-and-forward relays, the data may be leaked through the untrusted relays. Depending on a mathematical tool may affect the data security vulnerability. Thus, to enhance the system security, physical layer security is used to transmit a jamming signal to confuse the untrusted relay nodes. Hence, the proposed framework ensures security by combining the physical and data layer security which comes with a cost regarding system complexity, system latency, and energy consumption. The proposed framework addresses the joint problem of physical and data layer security, multicell association, task offloading, users’ power allocation, and service caching in multitier communication and edge computing networks. The objective is to minimize the system latency and energy consumption under the secrecy capacity constraint. Due to the NP-hard nature of the joint problem, we use a low-complexity Lyapunov drift-plus-penalty optimization technique based on the Gibbs sampling algorithm. The simulation results demonstrate the proposed framework’s superiority over the state-of-the-art in terms of high secrecy capacity and low computational complexity. When the secrecy capacity threshold increases, the secrecy capacity is enhanced by approximately 5.72%, while the system latency and energy consumption increase by 38.18% and 69.99%, respectively, compared to the literature.

This paper presents a performance analysis of the Age of Information ($A$oI) in full-duplex (FD) cooperative (C) non-orthogonal multiple access (NOMA) integrated with simultaneous wireless information and power transfer (FD-C-SWIPT-NOMA). Particularly, approximated closed-form expressions for the average block error rate (BLER) are derived for the proposed scheme and validated by Monte Carlo simulations. Based on the derived expressions of the average BLER, the expected weighted sum of AoI (EWSAoI) is obtained for the proposed scheme. Additionally, the proposed scheme is compared with other baseline schemes such as orthogonal multiple access (OMA), NOMA, and half duplex (HD) cooperative SWIPT NOMA schemes in terms of EWSAoI. Finally, the effect of variable power allocation, distances, and residual self-interference (RSI) on the AoI is analysed and simulation results demonstrate the superiority of the proposed scheme compared to baseline schemes in terms of the EWSAoI.

Accurate channel estimation is crucial for the proper operation of reconfigurable intelligent surfaces (RIS). This paper introduces a convolutional neural network (CNN) approach for multi-user RIS channel estimation that incorporates the zero-shot noise-to-noise (N2N) methodology within its architecture. In contrast to techniques that rely on clean training data, the proposed method learns from the noisy data itself to figure out how to remove the noise. The proposed zero-shot N2N self-learning demonstrates improved performance and a fast convergence rate in the RIS channel estimation.

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users’ QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation , optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks . The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput.

Abstract Cancelable template protection techniques are indispensable to provide essential security and privacy privileges in biometric systems. This paper introduces an efficient cancelable palmprint recognition technique based on multi-level transformations. Gabor filtering with feature remapping is introduced for extracting highly discriminative features. Histogram remapping is applied to nonlinearly transform the downsampled Gabor features to be normally distributed. This feature-remapping step is proposed to enhance the discriminatory power and alleviate the effect of feature variability and image artifacts. Comb filtering is applied to the mapped features as a first protection layer. To provide security guarantees against linkability attacks, index-based locality-sensitive hashing (LSH) is introduced as a second protection layer to transform the comb-filtered mapped real-valued features into maximum-ranked indices. Recognition is performed in the secured domain to accelerate matching and to preserve the user’s privacy. Results show that the proposed scheme provides a large re-issuance ability, protects templates from being inverted, and demonstrates strong unlinkability across different databases. In addition, favorable verification/identification accuracy is obtained and the system satisfies the needs for real-time applications. A global measure $${D}_{\leftrightarrow }^{sys}$$ D ↔ sys value of 0.01 is obtained, and thus, correlation attacks are mitigated. The recognition results for the legitimate scenario are 100% identification accuracy and 0% EER. For the worst case (same token scenario), the corresponding results are 99.752% and 0.31%, respectively.

In vehicular communications environments, channels are characterized as dynamic and highly mobile. As uch, estimating the vehicular communication channel with a massive number of antennas installed at the transmitter and receiver is considered a daunting task for conventional estimators and deep-learning approaches. Classical estimators provide inaccurate estimation results, and the deep learning algorithms require a huge dataset for training the model. This paper proposes a transfer learning and meta-learning approach for channel estimation in outdoor vehicular environments with millimeter-wave transmission frequencies above 6 GHz. The proposed system learns a good initialization of the model weight parameters using a few samples and a small number of gradient steps to achieve model convergence. Simulation results show that the proposed algorithm outperforms the conventional least square estimator in the outdoor millimeter-wave vehicular environments.

The integration of unmanned aerial vehicles (UAVs) into wireless networks is gaining significant attention in the fifth generation (5G) and beyond technologies. The use of UAVs has the potential of expanding coverage and providing efficient and reliable communication services, especially in remote and inaccessible areas. While other studies have explored sum-rate maximization via user pairing considering a multi-armed bandit (MAB) for a single UAV, the use of MAB in multiple UAVs especially under non-orthogonal multiple access (NOMA) scheme is not fully explored. This paper presents an algorithm for user pairing targeting sum-rate maximization of multi-UAV NOMA networks by applying multi-agent bandits that employ two-sided matching. The proposed method performs user association and power allocation with no coordination among the UAVs. The simulation results demonstrate the superior performance of the proposed method which is very close to that achieved by exhaustive search and outperforms random matching.

This paper investigates the performance of joint uplink-downlink communication of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network. Unlike most existing works that considered time-division duplexing (TDD) system to exploit the IRS uplink-downlink channel reciprocity, we adopt frequency-division duplexing (FDD) to achieve a fair trade-off between the signal reception reliability and system spectral efficiency. We analyze the system outage probability and outage-throughput, and derive their associated performance bounds in closed-form expressions. Moreover, for the second user in decoding order, we formulate two optimization problems over the IRS elements phase-shifts. The first optimization problem aims to maximize the minimum SNR in the uplink and downlink and the second optimization problem targets maximizing both SNRs. We employ genetic algorithms (GA) to solve the two problems. Monte-Carlo simulations are applied to validate the analytically driven bounds and to compare between the solutions of the proposed optimization problems.

Abstract Automatic detection and counting of vehicles in a video is a challenging task and has become a key application area of traffic monitoring and management. In this paper, an efficient real-time approach for the detection and counting of moving vehicles is presented based on YOLOv2 and features point motion analysis. The work is based on synchronous vehicle features detection and tracking to achieve accurate counting results. The proposed strategy works in two phases; the first one is vehicle detection and the second is the counting of moving vehicles. Different convolutional neural networks including pixel by pixel classification networks and regression networks are investigated to improve the detection and counting decisions. For initial object detection, we have utilized state-of-the-art faster deep learning object detection algorithm YOLOv2 before refining them using K-means clustering and KLT tracker. Then an efficient approach is introduced using temporal information of the detection and tracking feature points between the framesets to assign each vehicle label with their corresponding trajectories and truly counted it. Experimental results on twelve challenging videos have shown that the proposed scheme generally outperforms state-of-the-art strategies. Moreover, the proposed approach using YOLOv2 increases the average time performance for the twelve tested sequences by 93.4% and 98.9% from 1.24 frames per second achieved using Faster Region-based Convolutional Neural Network (F R-CNN ) and 0.19 frames per second achieved using the background subtraction based CNN approach (BS-CNN ), respectively to 18.7 frames per second.

Intelligent reflecting surface (IRS) can play a major role in relaying data in 6G networks. This paper studies the system performance of IRS selection (IRS-S) for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). We first present two selection schemes in NOMA scenario for IRS relays, namely, the two-stage and the max-min IRS-S strategy. Then, we drive a closed-form expression for the system outage probability for the two-stage IRS-S scheme and the OMA scenario. In addition, simulation results are provided to validate the analytically driven expressions of the outage probability for the proposed schemes. The results confirm that the two-stage IRS-S strategy has superior performance compared to all selection schemes in both NOMA and OMA setups.

This work proposes a new technique to reduce the phase noise (PN) and improve the tuning sensitivity of$K$-band voltage-controlled oscillators (VCOs) by increasing the quality ($Q$-) factor of the switched resonator. The proposed switched resonator consists of a high$Q$-factor half-circle inductor in parallel with an improved switched varactor and operates in four different frequency bands using a single switching voltage pin. The proposed switched resonator introduces one pole before the parallel resonance frequency, which sharpens the skirt characteristics of the scattering parameters of the resonator. The chip is implemented in the 0.18-$\mu \text{m}$CMOS technology, and the proposed VCO operates in four different frequency bands, with a total frequency tuning range (FTR) of 10.7%. The first band ranges from 19.4 to 19.84 GHz, the second band ranges from 19.8 to 20.3 GHz, the third band ranges from 20.25 to 20.73 GHz, and the fourth band ranges from 20.7 to 21.3 GHz. The VCO core circuit draws a dc current of 5 mA from a 1.8-V supply and achieves a PN of −110.7 at 1-MHz offset from a 20.25-GHz carrier. This proposed oscillator achieved a figure of merit (FoM) of −186.8 dBc/Hz, while the FoM taking account of the tuning voltage (FoMT/V) is −181.4 dBc/Hz.

In this paper, we investigate the mutual mobility effect for independent multiple primary users and numerous secondary users on the spectrum sensing capability of secondary users. The proposed system model adopts two different mobility models; namely, the random walk (RWL) model and random waypoint (RWP) model with varying numbers of primary mobile users and secondary mobile users in the network. Closed-form expressions for the detection capability and normalized sensing capacity were derived. The results show that the increase in the number of independent mobile users following a random mobility model has an impact on the total sensing capacity over unoccupied bands.

Spectrum sensing task is an essential operation in cognitive radio networks. As such, this paper considers the impact of primary mobile users whose location and dynamic spectrum use can be tracked and sensed by collaborative secondary users. We evaluate the probability of detection for two simplistic random mobility models by acquiring compressed signal measurements at the fusion center. The collaborative signal acquisition is to reduce the computation at the secondary user side. Combined with the recovered signal via compressed sensing, a localization technique by Kalman filtering is used to track a primary mobile user in the network region. For a random mobility model, it is shown that better spectrum sensing performance can be achieved with a high probability of detection. Further, for pseudo-static movements having pause time factor, the detection performance increases. Simulation results are given to corroborate the approach used in the evaluation.

The introduction of cognitive radio technology in licensed 5G networks could significantly enhance the overall system capacity and number of served users. In this context, this work discusses the admission of new cognitive radio secondary users (SUs) in the fifth generation (5G) Heterogeneous Networks (HetNets) as well as the allocation of the channels over the secondary cognitive network, using a many-to-one matching game and auctions theory. Simulation results show that the used matching algorithm for users admission is of low complexity as well as the existence of a Walrasian equilibrium point for the channels allocation problem.

This paper investigates the secrecy performance of a multiuser system that utilizes transmit antenna selection scheme at the base station and adopts threshold-based selection diversity opportunistic scheduling over legitimate nodes. The legitimate transmission suffers from the presence of noncolluding (Non-Col) and colluding (Col) multiple passive eavesdroppers. Both the legitimate and eavesdropping nodes are assumed to suffer from co-channel interference (CCI) signals from independent channels that follow Rayleigh fading. Closed-form expressions for the probability density functions and cumulative density functions of the end-to-end signal-to-interference-plus-noise ratio for both eavesdropping scenarios in the presence of CCI signals are derived. In addition, closed-form expressions for the network secrecy outage probability (SOP) for Non-Col/Col scenarios are derived. At the high signal-to-noise ratio values, closed-form expressions for the asymptotic secrecy outage probabilities are obtained. Following this obtained asymptotic analysis, an optimization problem for power allocation is formulated and solved to improve the secrecy performance of the network by minimizing the asymptotic SOP for both Col and Non-Col cases. The derived analytical expressions are then validated using both simulations and numerical results.

Non-Orthogonal Multiple Access (NOMA) is a promising multiple access technique for the 5thgeneration (5G) mobile communication systems. In this paper, an energy efficient power allocation scheme is derived for a general number of users per cluster multiple input multiple output (MIMO) downlink NOMA system. The proposed scheme idea is based on converting the difficult energy efficiency power allocation problem into an equivalent spectral efficiency power allocation problem and then dividing this equivalent problem into multiple simple cluster sum rate maximization problems. In addition, a user clustering scheme is proposed to maximize the NOMA system energy efficiency by first designing the detection vectors to convert the MIMO users channel matrices into their equivalent channel vectors and distribute the users on the clusters based on their equivalent channel gains. The users are clustered such that those with the largest equivalent channel gains are selected as cluster heads and the rest of users in each cluster are selected to maximize the equivalent channel gain difference between each other. Numerical results show that the proposed framework improves the system energy efficiency of the MIMO NOMA system at different values of the total transmit power, the minimum required date rates, the number of users per cluster and at different users' distance distribution scenarios.

This paper investigates the secrecy performance of a multiuser threshold-based transmit antenna selection scheme (TAS/tSD) in the presence of co-channel interference (CCI) signals and the existence of a passive eavesdropping node. In particular, an exact closed-form expression for the secrecy outage probability is derived. Then, the asymptotic secrecy outage closed-form expression is obtained at the high signal-to-noise ratio (SNR) values. Based on the obtained asymptotic analysis, an optimization problem for power allocation is formulated and solved to improve the secrecy performance of the system concerning minimizing the asymptotic secrecy outage probability. Numerical and simulation results are obtained to justify the obtained analysis.

Introducing mobility to Wireless Sensor Networks (WSNs) puts new challenges particularly in designing of routing protocols. Mobility can be applied to the sensor nodes and/or the sink node in the network. Many routing protocols have been developed to support the mobility of WSNs. These protocols are divided depending on the routing structure into hierarchical-based, flat-based, and location-based routing protocols. However, the hierarchical-based routing protocols outperform the other routing types in saving energy, scalability, and extending lifetime of Mobile WSNs (MWSNs). Selecting an appropriate hierarchical routing protocol for specific applications is an important and difficult task. Therefore, this paper focuses on reviewing some of the recently hierarchical-based routing protocols that are developed in the last five years for MWSNs. This survey divides the hierarchical-based routing protocols into two broad groups, namely, classical-based and optimized-based routing protocols. Also, we present a detailed classification of the reviewed protocols according to the routing approach, control manner, mobile element, mobility pattern, network architecture, clustering attributes, protocol operation, path establishment, communication paradigm, energy model, protocol objectives, and applications. Moreover, a comparison between the reviewed protocols is investigated in this survey depending on delay, network size, energy-efficiency, and scalability while mentioning the advantages and drawbacks of each protocol. Finally, we summarize and conclude the paper with future directions.

In this letter, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this letter confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.