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Mobile and wearable devices are now the main part of our lives. The power consumed by these devices is usually in the range of μW or mW. Due to the requirement of periodic recharging, this work tries to present an economic renewable energy harvesting source for the process of charging. In this paper, authors exploit a huge amount of energy dissipated daily in the form of loud noise through streets up to 85 dB to generate a sufficient rate of energy to recharge the lithium batteries of wearable and mobile devices (more than 4.01 V). The piezoelectric model 7BB-27-4 was used in this work through a proposed design circuit. Suitable software was used to simulate the design. In comparison to previous research findings, the authors' findings are sufficiently satisfactory. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a trajectory planning algorithm (TPA) in aerial/unmanned aerial vehicle- (UAV-) aided communications using a practical budget-constrained multiarmed bandit (BC-MAB) in disaster regions. Hence, we propose two cost-efficient TPAs based on two variants of the upper confidence bound (UCB) algorithms, namely, UCB-BC1 and UCB-BC2, via orthogonal multiple access (OMA) transmission. The former assumes prior information about the minimum expected costs, while the latter estimates the minimum costs from empirical observations. Simulation results confirm that the proposed algorithms outperform other benchmark schemes in terms of the total number of assisted survivors, battery consumption, and convergence speed. [ABSTRACT FROM AUTHOR]

The design and investigation of an intelligent controller for hardware-in-the-loop (HIL) implementation of hybrid electric vehicles (HEVs) are proposed in this article. The proposed intelligent controller is adopted based on the enhancement of a model predictive controller (MPC) by an artificial neural network (ANN) approach. The MPC-based ANN (NNMPC) is proposed to control the speed of HEVs for a simulation system model and experimental HIL test systems. The HIL is established to assess the performance of the NNMPC to control the velocity of HEVs in an experimental environment. The real-time environment of HIL is implemented through a low-cost approach such as the integration of an Arduino Mega 2560 and a host Lenovo PC with a Core i7 @ 3.4 GHz processor. The NNMPC is compared with a proportional–integral (PI) controller, a classical MPC, and two different settings of the ANN methodology to verify the efficiency of the proposed intelligent NNMPC. The obtained results show a distinct behavior of the proposed NNMPC to control the speed of HEVs with good performance based on the distinct transient response, minimum error steady state, and system robustness against parameter perturbation. [ABSTRACT FROM AUTHOR]

Several security tools have been described in recent times to assist security teams; however, the effectiveness and success remain limited to specific devices. Phishing is a type of cyberattack that uses fraudulent emails and websites to obtain personal information from unsuspecting users, such as passwords and credit card numbers. Hackers can gain access to your information through a variety of methods, and the most common of which are king, phishing, spear phishing, social engineering, and dictionary attacks. Each of these techniques is unique, but they all have the same goal: to obtain your personal information. Nevertheless, there is the potential to exploit this problem in terms of security. In this paper, we used the Bash Bunny (BB), a new tool designed to assist military, law enforcement, and penetration tester teams with their work to conduct exfiltration without privilege escalation through T1200, T1052, and T1052.001 techniques in air-gapped networks with effectiveness/success 99.706%. [ABSTRACT FROM AUTHOR]

Millimetre wave (mmWave) communications, that is, 30 to 300 GHz, have intermittent short‐range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mmWave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two‐phase multi‐armed bandit (MAB) game. In the first phase, the PS vector of the mmWave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy (MOSS) MAB algorithm is utilized to implement the proposed two‐phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information (CSI) of both mmWave BS and RIS, codebook‐based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios. [ABSTRACT FROM AUTHOR]

With the gradual promotion of the fifth generation (5G) mobile communication and Internet of Things (IoT) applications, wireless communication transmission will be more vulnerable to illegal interceptions and/or attacks. To ensure communication security, we study covert communication of downlink nonorthogonal multiple access (NOMA) systems, where the channel knowledge of users is uncertain. A multiantenna transmitter tries to covertly transmit information to a covert user (strong user) through the shield of a public communication link (weak user), while the warden tries to detect the communication behavior between the transmitter and the covert user. To improve security and energy efficiency, the ${k}$ th best antenna of the transmitter is selected, since the optimal antenna may be not available due to some schedule and/or other reasons. Aiming to evaluate the proposed framework performance, we start by deriving exact expressions for the minimum detection error probability and the optimal detection threshold of the warden, followed by a calculation analysis of the expected minimum detection error probability (EMDEP) and the outage probability of NOMA users. The asymptotic behavior for the outage probability is investigated at high signal-to-noise ratio (SNR) to acquire greater useful insights. With the goal of improving the system covertness performance, we propose that a scheme is optimized to enhance the covert throughput of the system to the maximum. Simulation results show that the following hold: 1) channel estimation errors have a significant effect on system performance; 2) reliability performance tends to build up, as the total number of antennas grows large; and 3) as the transmitting power and number of antennas increases, there is an upper bound for maximizing the covert throughput. [ABSTRACT FROM AUTHOR]

The integration of Ambient Backscatter Communication (AmBC) with Non-Orthogonal Multiple Access (NOMA) is expected to support connectivity of low-powered Internet-of-Vehicles (IoVs) in the upcoming Sixth-Generation (6G) transportation systems. This paper proposes an energy-efficient resource allocation framework for the AmBC-enabled NOMA IoV network under imperfect Successive Interference Cancellation (SIC) decoding. In particular, multiple Road-Side Units (RSUs) transmit superimposed signals to their associated IoVs utilizing downlink NOMA transmission. Meanwhile, the Backscatter Tags (BackTags) also transmit data symbols towards nearby IoVs by reflecting the superimposed signals of RSUs. Thus, the objective is to maximize the total energy efficiency of the NOMA IoV network subject to the minimum data rate of all IoVs. A joint problem that simultaneously optimizes the total power budget of each RSU, power allocation coefficient of IoVs and reflection power of BackTags under imperfect SIC decoding is formulated. A Dinkelbach approach is first adopted to transform the optimization problem and then the transformed problem is decoupled into two subproblems for optimal transmit power at RSUs and efficient reflection power at BackTags, respectively. To solve the problems efficiently, dual theory and Karush-Kuhn-Tucker conditions are exploited, where the Lagrangian dual variables are iteratively calculated using the subgradient method. To check the performance of the proposed framework, a benchmark optimization without AmBC is also provided. Numerical results demonstrate the superiority of the proposed AmBC-enabled NOMA IoV framework over the benchmark conventional IoV framework. [ABSTRACT FROM AUTHOR]

The hybrid combination between underwater optical wireless communication (UOWC) and radio frequency (RF) is a vital demand for enabling communication through the air–water boundary. On the other hand, non-orthogonal multiple access (NOMA) is a key technology for enhancing system performance in terms of spectral efficiency. In this paper, we propose a downlink NOMA-based dual-hop hybrid RF-UOWC with decode and forward (DF) relaying. The UOWC channels are characterized by exponential-generalized Gamma (EGG) fading, while the RF channel is characterized by Rayleigh fading. Exact closed-form expressions of outage probabilities and approximated closed-form expressions of ergodic capacities are derived, for each NOMA individual user and the overall system as well, under the practical assumption of imperfect successive interference cancellation (SIC). These expressions are then verified via Monte-Carlo simulation for various underwater scenarios. To gain more insight into the system performance, we analyzed the asymptotic outage probabilities and the diversity order. Moreover, we formulated and solved a power allocation optimization problem to obtain an outage-optimal performance. For the sake of comparison and to highlight the achievable gain, the system performance is compared against a benchmark orthogonal multiple access (OMA)-based system. [ABSTRACT FROM AUTHOR]

The mixture of wireless and power line communications (PLC) is vital for implementing new applications in smart grid and vehicular communications. In this work, we investigate the performance of non-orthogonal multiple access (NOMA) based dual-hop hybrid communication systems with decode-and-forward relay. The wireless channel is characterized by Nakagami-m fading under an additive white Gaussian noise (AWGN), while the PLC channels are characterized by Log-normal distribution with Bernoulli Gaussian noise including both background and impulsive noise components. New closed-form expressions for the outage probability, the asymptotic outage probability and ergodic capacity are derived and verified via extensive representative simulations. For more insights on the outage performance, we analyze the diversity order. Additionally, we proposed a power allocation optimization technique to achieve an outage-optimal performance. The results show that the system outage probability improves as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise decrease, while their effect is negligible on the ergodic capacity. Finally, the performance of the proposed system is compared against a benchmark OMA-based system. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF’s future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones. [ABSTRACT FROM AUTHOR]

Achieving high network traffic demand in limited spectrum resources is a technical challenge for the beyond 5G and 6G communication systems. To this end, ambient backscatter communication (AmBC) is proposed for Internet-of-Things (IoT), because the backscatter device (BD) can realize communication without occupying extra spectrum resources. Moreover, the spectral efficiency can be further improved by using two-way (TW) communication. However, secure communication is a great challenge for accessing massive IoT devices due to the broadcasting nature of wireless propagation environments. In light of this fact, this article proposed a two-way ambient backscatter communication (TW-AmBC) network with an eavesdropper. Specifically, the physical layer security (PLS) is studied through deriving the analytical/asymptotic expressions of the outage probability (OP) and intercept probability (IP). Moreover, the outage probabilities (OPs) in high signal-to-noise ratio (SNR) regions are studied for the asymptotic behavior and the intercept probabilities (IPs) in high main-to-eavesdropping ratio (MER) regions as well. Through analysis and evaluation of simulation performance, the results show that (i) when considering the target node, the OP of the BD decreases with increasing SNR, that is, enhancing the reliability; (ii) an optimal value of BD's reflection coefficient that maximize the reliability of backscatter link can be obtained; (iii) in high SNR regions, the OPs approach a constant; thus, the diversity orders are zero; (iv) when increasing the MER, the IP of target node decreases, suggesting the security enhances; (v) a trade-off exists between reliability and security which can be optimized by carefully designing the parameters. [ABSTRACT FROM AUTHOR]

Node localization is one of the major challenges that exist in underwater communication. Various techniques exist for terrestrial networks, while few of them are applicable in underwater networks due to the dynamic characteristic of the underwater channels, e.g., the lack of global positioning system (GPS) coverage under the water surface. Moreover, assorted environmental properties affect almost all employed communication techniques. In this letter, we propose an environmental-aware positioning system by considering the variations of the underwater speed of sound according to the dynamic changes in the physical properties of the seawater, such as temperature, salinity, and pressure, besides the internal waves’ effects. The proposed system employs the received signal strength (RSS) technique in estimating the distances between the network nodes. Moreover, we examine the application of various dynamic responses neural networks (NNs) in predicting the underwater node position, such as the feedforward, recurrent, time delay, and distributed delay NNs. The results show that the NN-based prediction models enhance the performance of the positioning system and could achieve small prediction errors in the range of 0.002 for both training and testing patterns. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and ambient backscatter communication (AmBC) have gained a lot of interest as key enabling technologies in wireless communications systems. NOMA is a key player for enhancing spectrum utilization either by multiplexing multiple messages for the same user or allowing multi-users access, while AmBC shows great potential for enhancing both spectrum and energy efficiency for battery-limited devices. In this paper, the performance of a downlink NOMA multiplexing based symbiotic-radio ($SR$) AmBC system is analyzed over Nakagami-m fading channels. New closed-form expressions for the exact and asymptotic outage probabilities are derived. Moreover, we analyze the diversity order and the influence of the system parameters on the outage performances. Besides, we proposed a power allocation optimization technique to achieve an outage-optimal performance. Through representative Monte-Carlo simulations, we have verified the analytical results. Finally, we compared the performance of the proposed system against a benchmark OMA-based system. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC) has emerged as a promising paradigm for enabling sustainable low-power operation of Internet of Things devices. This is due to its ability to enable sensing and communication through backscattering ambient wireless signals (e.g., WiFi and TV sig-nals). But a great impediment to AmBC-enabled networks is the difficulty in decoding the backscat-ter signals because the ambient signals are usually modulated and meant for other legacy receivers rather than AmBC devices. Drawing from the ability of machine learning (ML) to enhance the performance of wireless communication systems, some ML-aided techniques have been developed to assist signal detection in AmBC. Hence, this article aims to provide a comprehensive overview of the subject by describing the operation of the AmBC network, highlighting the major challenges to signal detection in AmBC, discussing and com-paring the performance of some existing ML-assisted solutions to AmBC signal detection, and highlighting some future research that could be carried out on the subject. [ABSTRACT FROM AUTHOR]

The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels of the largest blob of a digital image. The proposed algorithms are tested using several experiments and techniques, which are followed by uploading the watermarked images into a socialmedia site to evaluate the probability of extracting the embedding watermarks. The results show that the proposed approaches outperformthe traditional LSB algorithmin terms of time, evaluation criteria and the percentage of pixels that have changed. [ABSTRACT FROM AUTHOR]

Despite extensive research efforts, underwater sensor networks (UWSNs) still suffer from serious performance issues due to their inefficient and uncoordinated channel access and resource management. For example, due to the lack of holistic knowledge on the network resources, existing decentralized routing protocols fail to provide globally optimal performance. On the other hand, Software Defined Networking (SDN), as a promising paradigm to provide prominent centralized solutions, can be employed to address the aforementioned issues in UWSNs. Indeed, SDN brings unprecedented opportunities to improve the network performance through the development of advanced algorithms at controllers. In this paper, we study the routing problem in such a network with new features including centralized route decision, global network-state awareness, seamless route discovery while considering the optimization of several long-term global performance metrics. We formulate the entire routing problem of a multi-modal UWSN as an optimization problem while considering the interference phenomenon of ad hoc scenarios and some long-term global performance metrics of an ideal routing protocol. Our formulated problem nicely captures all possible flexibilities of a sensor node no matter it has the full-duplex or half-duplex functionality. Upon the formulation, we recognize the NP-hard nature of the problem for all possible scenarios. We adopt a rounding technique based on the convex programming relaxation concept to solve the formulated routing problem that considers full-duplex scenarios, whereas we solve the problem for half-duplex scenarios using a greedy method upon interpreting it as a submodular function maximization problem. Through extensive simulation via our Python-based in-house simulator, we verify that our proposed globally optimal routing scheme always outperforms three existing decentralized routing protocols (each of these protocols are selected from each of three prominent protocol types, i.e., flooding, cross-layer information and adaptive machine learning based, respectively) in terms of reliability, latency, energy efficiency, lifetime and fairness. [ABSTRACT FROM AUTHOR]

Underwater is a harsh and dynamic environment. Its physical properties are unsteady in the space-time domain and that complicates the communication process inside the water. Moreover, underwater transmissions suffer from many attenuations and scattering sources due to several environmental noises such as shipping, turbulence, wave dynamics, and the background life of the marine organisms. Therefore, opportunistic and cooperative communications are necessary to compensate for the prompt changes in underwater environments. In this paper, we propose a new opportunistic cooperative transmission scheme for underwater networks that is adaptable for various environmental conditions. The proposed scheme employs the key physical variables of the seawater, e.g. temperature, salinity, density, and waves speed, to indicate the proper relaying scheme jointly with the appropriate modulation method and the transmitting power level that suit the current transmission situations. The paper also proposes a study that demonstrates the effects of these variables on the underwater speed of sound which is the most commonly used carrier for underwater communication and hence affect the transmitted signal intensity. The results show that the proposed opportunistic cooperative transmission scheme provides an efficient compromise between the overall network performance and the energy efficiency at the nodes. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) has emerged as a promising technology for 5G networks and beyond. In order to fully reap the benefits of NOMA, it is essential to characterize its performance for different channel conditions. In this paper, we carry out a performance analysis of downlink NOMA systems subject to $\kappa$ - $\mu$ shadowed fading channels. Specifically, ergodic capacity, outage probability, and average bit error rate expressions for a two-users NOMA scheme under $\kappa$ - $\mu$ shadowed fading links are derived. The accuracy of the analytical results has been validated by extensive simulations. [ABSTRACT FROM AUTHOR]

Multi-hop communication has attracted a lot of attention recently due to its ability to extend the coverage range and to overcome blockage. In this letter, we propose a machine learning-based selection approach that adaptively chooses the best forwarding scheme in hybrid multi-hop dense networks. The proposed transmission scheme employs a multi-layer selection where each layer represents one possible relaying case, namely amplify-and-forward, half-detection, or full-detection of the transmitted symbol, or even no-relaying. Moreover, the proposed system dynamically learns the proper forwarding scheme out of these layers for each involved relay to minimize the transmission error rate based on the relay location, and its residual energy. A heuristic approach is proposed for the forwarding scheme selection and transmission power control where a minimum threshold value for the transmission power of each relay node is derived in order to satisfy a target QoS requirement. The results are used for training a decision trees-based prediction model that achieves a remarkable accuracy beyond the 99% for both training and testing patterns. [ABSTRACT FROM AUTHOR]

Vehicular communications in intelligent transportation systems promise to improve traffic efficiency, road utilization, and safety. Achieving secure vehicular communications is vital for the deployment of vehicle-to-everything (V2X) applications. This article provides a comprehensive overview of physical-layer security (PLS) strategies employed for V2X. By exploiting the randomness and the physical characteristics of wireless channels, PLS offers reliable solutions against eavesdropper attacks as complementary approaches to cryptographic techniques. We give a brief introduction to the architecture and characteristics of V2X networks. Then the fundamental principles of PLS technology are proposed, followed by security threats in V2X. Additionally, we discuss some open issues and challenges for future research. Finally, we investigate a case study where different challenges and technologies coexist in one V2X scenario. [ABSTRACT FROM AUTHOR]

This paper considers an analytical approach to evaluate the outage behavior of the device-to-device (D2D) communication, which is underlaid with cellular networks, as an enabling technology for Internet-of-Things (IoTs). In such an architecture, a group of IoT devices (IoTDs) communicate with an IoT gateway by reusing the resources of cellular users (CUEs) to enhance the spectral efficiency of the fifth-generation networks. Two interference management schemes are widely used in the literature for the sharing of D2D spectrum, namely the fixed-power margin (FPM) and the cooperative pairing (CooP) schemes. We investigate and compare the performance of the two schemes from the perspective of outage probability (OP). While satisfying the minimal performance of the system, the OP of an arbitrary pair (i.e., one IoTD and one CUE) under both schemes are derived in closed form in terms of hyper-geometric functions via the Mellin transform technique. Moreover, for the CooP scheme, an iterative alternating Dinkelbach algorithm is proposed as an outage-optimal power allocation scheme. Analytical and simulation results reveal that the CooP scheme is the outage optimum for the high SNR regime while the FPM scheme is the optimal one for the low SNR regime. Simulation results also show that the suitable power margin of the FPM scheme lies in between 2 and 3 dB. Under these two interference management schemes, the accuracy of the analytical results is verified through numerical simulation and it turns out that these are well matched. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS. [ABSTRACT FROM AUTHOR]