Search

This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, one or more cell-center users (CCU), and multiple cell-edge users (CEU) located at the edge of the cells. Through JT-CoMP, all the BSs collaborate to simultaneously transmit the data to all the users including the CCUs and CEUs. To enhance the signal quality for the CEUs, CCUs relay the common stream to the CEUs by operating in either half-duplex (HD) or full-duplex (FD) decode-and-forward (DF) relaying mode. In this setup, we aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCUs, and the time slot fraction, aiming to maximize the minimum achievable data rate. However, the formulated optimization problem is non-convex and is challenging to solve directly. To address this challenge, we employ change-of-variables, first-order Taylor approximations, and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate through simulation results the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD/FD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show that the proposed FD C-RSMA can achieve 25% over FD C-NOMA and the proposed HD C-RSMA can achieve 19% over HD C-NOMA respectively, when the BS transmit power is 20 dBm., Comment: 14 pages

Honeypot technologies provide an effective defense strategy for the Industrial Internet of Things (IIoT), particularly in enhancing the Advanced Metering Infrastructure's (AMI) security by bolstering the network intrusion detection system. For this security paradigm to be fully realized, it necessitates the active participation of small-scale power suppliers (SPSs) in implementing honeypots and engaging in collaborative data sharing with traditional power retailers (TPRs). To motivate this interaction, TPRs incentivize data sharing with tangible rewards. However, without access to an SPS's confidential data, it is daunting for TPRs to validate shared data, thereby risking SPSs' privacy and increasing sharing costs due to voluminous honeypot logs. These challenges can be resolved by utilizing Federated Learning (FL), a distributed machine learning (ML) technique that allows for model training without data relocation. However, the conventional FL algorithm lacks the requisite functionality for both the security defense model and the rewards system of the AMI network. This work presents two solutions: first, an enhanced and cost-efficient FedAvg algorithm incorporating a novel data quality measure, and second, FedPot, the development of an effective security model with a fair incentives mechanism under an FL architecture. Accordingly, SPSs are limited to sharing the ML model they learn after efficiently measuring their local data quality, whereas TPRs can verify the participants' uploaded models and fairly compensate each participant for their contributions through rewards. Simulation results, drawn from realistic mircorgrid network log datasets, demonstrate that the proposed solutions outperform state-of-the-art techniques by enhancing the security model and guaranteeing fair reward distributions., Comment: Accepted for IEEE TNSM

In this paper, we tackle the issue of moral hazard within the realm of the vehicular Metaverse. A pivotal facilitator of the vehicular Metaverse is the effective orchestration of its market elements, primarily comprised of sensing internet of things (SIoT) devices. These SIoT devices play a critical role by furnishing the virtual service provider (VSP) with real-time sensing data, allowing for the faithful replication of the physical environment within the virtual realm. However, SIoT devices with intentional misbehavior can identify a loophole in the system post-payment and proceeds to deliver falsified content, which cause the whole vehicular Metaverse to collapse. To combat this significant problem, we propose an incentive mechanism centered around a reputation-based strategy. Specifically, the concept involves maintaining reputation scores for participants based on their interactions with the VSP. These scores are derived from feedback received by the VSP from Metaverse users regarding the content delivered by the VSP and are managed using a subjective logic model. Nevertheless, to prevent ``good" SIoT devices with false positive ratings to leave the Metaverse market, we build a vanishing-like system of previous ratings so that the VSP can make informed decisions based on the most recent and accurate data available. Finally, we validate our proposed model through extensive simulations. Our primary results show that our mechanism can efficiently prevent malicious devices from starting their poisoning attacks. At the same time, trustworthy SIoT devices that had a previous miss-classification are not banned from the market., Comment: Accepted in WCNC 2024

Underwater communication networks are increasingly popularized by various important maritime applications. However, this also leads to an increased threat landscape. This letter presents the first study that considers jamming attacks by random jammers present in the surroundings of legitimate transceivers in underwater acoustic communication systems. We investigate the impact of jamming attacks on various performance parameters of the legitimate underwater acoustic communication link. In particular, we investigate the legitimate link using stochastic geometry for important performance parameters, namely coverage probability, average rate, and energy efficiency of the link between two legitimate nodes, i.e., underwater and surface nodes. We then derive and present tractable expressions for these performance parameters. Finally, we performed a Monte Carlo simulation to validate our analysis. We plot the performance metrics against the transmit power, and jamming power for different intensities of the jammers in shallow, mid, and deep water scenarios. Results reveal that on average, jamming in deep water has a relatively high impact on the performance of legitimate link than in shallow water.

High altitude platforms (HAPs)-aided terrestrial-aerial communication technology based on free-space optical (FSO) and Terahertz (THz) feeder links has been attracting notable interest recently due to its great potential in reaching a higher data rate and connectivity. Nonetheless, the presence of harsh vertical propagation environments and potential aerial eavesdroppers are two of the main challenges limiting the reliability and security of such a technology. In this work, a secrecy-enhancing scheme for HAP-aided ground-aerial communication is proposed. The considered network consists of HAP-assisted communication between a ground station and a legitimate user under the threat of an aerial and ground eavesdropper. Thus, the proposed scheme leverages (i) HAP diversity by exploiting the presence of multiple flying HAPs and (ii) the use of a hybrid FSO/THz transmission scheme to offer better resilience against eavesdropping attacks. An analytical secrecy outage probability (SOP) expression is derived for the scheme in consideration. Results manifest the notable gain in security of the proposed scheme with respect to both (i) the single-HAP and (ii) THz feeder-based benchmark ones, where the proposed scheme's SOP is decreased by four orders of magnitude using $4$ HAPs with respect to the first benchmark scheme, while a $5$-dB secrecy gain is manifested with respect to the second benchmark one., Comment: 14 pages, 9 figures

This paper explores a novel Neural Network (NN) architecture suitable for Beamformed Fingerprint (BFF) localization in a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) outdoor system. The mmWave frequency bands have attracted significant attention due to their precise timing measurements, making them appealing for applications demanding accurate device localization and trajectory estimation. The proposed NN architecture captures BFF sequences originating from various user paths, and through the application of learning mechanisms, subsequently estimates these trajectories. Specifically, we propose a method for trajectory estimation, employing a transformer network (TN) that relies on attention mechanisms. This TN-based approach estimates wireless device trajectories using BFF sequences recorded within a mmWave MIMO outdoor system. To validate the efficacy of our proposed approach, numerical experiments are conducted using a comprehensive dataset of radio measurements in an outdoor setting, complemented with ray tracing to simulate wireless signal propagation at 28 GHz. The results illustrate that the TN-based trajectory estimator outperforms other methods from the existing literature and possesses the ability to generalize effectively to new trajectories outside the training dataset., Comment: 6 pages, 7 figures, conference WCNC 2024

The colossal evolution of wireless communication technologies over the past few years has driven increased interest in its integration in a variety of less-explored environments, such as the underwater medium. In this magazine paper, we present a comprehensive discussion on a novel concept of routing protocol known as cross-media routing, incorporating the marine and aerial interfaces. In this regard, we discuss the limitation of single-media routing and advocate the need for cross-media routing along with the current status of research development in this direction. To this end, we also propose a novel cross-media routing protocol known as bubble routing for autonomous marine systems where different sets of AUVs, USVs, and airborne nodes are considered for the routing problem. We evaluate the performance of the proposed routing protocol by using the two key performance metrics, i.e., packet delivery ratio (PDR) and end-to-end delay. Moreover, we delve into the challenges encountered in cross-media routing, unveiling exciting opportunities for future research and innovation. As wireless communication expands its horizons to encompass the underwater and aerial domains, understanding and addressing these challenges will pave the way for enhanced cross-media communication and exploration., Comment: Submitted to IEEE Communications Magazine

Research in underwater communication is rapidly becoming attractive due to its various modern applications. An efficient mechanism to secure such communication is via physical layer security. In this paper, we propose a novel physical layer authentication (PLA) mechanism in underwater acoustic communication networks where we exploit the position/location of the transmitter nodes to achieve authentication. We perform transmitter position estimation from the received signals at reference nodes deployed at fixed positions in a predefined underwater region. We use time of arrival (ToA) estimation and derive the distribution of inherent uncertainty in the estimation. Next, we perform binary hypothesis testing on the estimated position to decide whether the transmitter node is legitimate or malicious. We then provide closed-form expressions of false alarm rate and missed detection rate resulted from binary hypothesis testing. We validate our proposal via simulation results, which demonstrate errors' behavior against the link quality, malicious node location, and receiver operating characteristic (ROC) curves. We also compare our results with the performance of previously proposed fingerprint mechanisms for PLA in underwater acoustic communication networks, for which we show a clear advantage of using the position as a fingerprint in PLA., Comment: Accepted for Presentation in IEEE VTC Spring 2023

The demand for high data rates is rapidly increasing as the interest in Magnetic Induction (MI) communication-based underwater applications grow. However, the data rate in MI is limited by the use of low operational frequency in generating a quasi-static magnetic field. In this paper, we propose the use of full-duplex (FD) MI communication to efficiently utilize the available bandwidth and instantly double the data rate. We propose a two-dimensional transceiver architecture to achieve full-duplex communication by exploiting the directional nature of magnetic fields. We further evaluate the proposed end-to-end FD MI communication against self-interference (SI), its impact on communication distance, and robustness in view of orientation sensitivity. Finally, we conclude by discussing typical challenges in the realization of FD MI communication and highlight a few potential future research directions., Comment: 6 pages, 6 figures, and submitted to IEEE Wireless Communications Magazine

In this paper, we quantify the physical layer security of a dual-hop regenerative relaying-based wireless communication system assisted by reconfigurable intelligent surfaces (RISs). In particular, the setup consists of a source node communicating with a destination node via a regenerative relay. In this setup, a RIS is installed in each hop to increase the source-relay and relay-destination communications reliability, where the RISs' phase shifts are subject to quantization errors. The legitimate transmission is performed under the presence of a malicious eavesdropper attempting to compromise the legitimate transmissions by overhearing the broadcasted signal from the relay. To overcome this problem, we incorporate a jammer to increase the system's secrecy by disrupting the eavesdropper through a broadcasted jamming signal. Leveraging the well-adopted Gamma and Exponential distributions approximations, the system's secrecy level is quantified by deriving approximate and asymptotic expressions of the secrecy intercept probability (IP) metric in terms of the main network parameters. The results show that the secrecy is enhanced significantly by increasing the jamming power and/or the number of reflective elements (REs). In particular, an IP of approximately $10^{-4}$ can be reached with $40$ REs and $10$ dB of jamming power-to-noise ratio even when the legitimate links' average signal-to-noise ratios are $10$-dB less than the eavesdropper's one. We show that cooperative jamming is very helpful in strong eavesdropping scenarios with a fixed number of REs, and the number of quantization bits does not influence the secrecy when exceeding $3$ bits. All the analytical results are endorsed by Monte Carlo simulations.

In this paper, the physical layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node $(S)$ communicates with a destination node $(D)$ through a decode-and-forward relay $(R)$, in the presence of two wiretappers $\left(E_{1},E_{2}\right)$ independently trying to compromise the two hops. Furthermore, the transmit nodes $\left(S,R\right) $ have a single transmit antenna, while the receivers $\left(R,D,E_{1},E_{2}\right) $ are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities' statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely (i) mobile nodes with imperfect CSI and (ii) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a $15$ dB secrecy loss at IP$=3\times10^{-3},$ when the CSI imperfection power raises from $0$ to $10^{-3}$. Lastly, the higher the legitimate nodes' speed, carrier frequency, delay, and/or relay's decoding threshold SNR, the worse is the system's secrecy. Monte Carlo simulations endorse the derived analytical results.

Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images., Comment: Accepted at GoodBrotherVI4IAAL Workshop @ICIAP2021

In this paper, we investigate for the first time the dynamic power allocation and decoding order at the base station (BS) of two-user uplink (UL) cooperative non-orthogonal multiple access (C-NOMA)-based cellular networks. In doing so, we formulate a joint optimization problem aiming at maximizing the minimum user achievable rate, which is a non-convex optimization problem and hard to be directly solved. To tackle this issue, an iterative algorithm based on successive convex approximation (SCA) is proposed. The numerical results reveal that the proposed scheme provides superior performance in comparison with the traditional UL NOMA. In addition, we demonstrated that in UL C-NOMA, decoding the far NOMA user first at the BS provides the best performance.

LoRa wireless networks are considered as a key enabling technology for next generation internet of things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this paper, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This paper proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system's quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on Reinforcement Learning (RL), are developed. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks., Comment: IEEE Internet of Things Journal, to appear

In this paper, we investigate the dynamic power allocation for a visible light communication (VLC) cellular system consisting of two coordinating attocells, each equipped with one access-point (AP). The coordinated multipoint (CoMP) between the two cells is introduced to assist users experiencing high inter-cell-interference (ICI). Specifically, the coordinated zero-forcing (ZF) precoding is used to cancel the ICI at the users located near the centers of the cells, whereas the joint transmission (JT) is employed to eliminate the ICI at the users located at the edge of both cells and to improve their receptions as well. Furthermore, two multiple access techniques are invoked within each cell, namely, non-orthogonal-multiple-access (NOMA) and cooperative non-orthogonal-multiple-access (C-NOMA). Hence, two multiple access techniques are proposed for the considered multi-user multi-cell system, namely, the CoMP-assisted NOMA scheme and the CoMP-assisted C-NOMA scheme. For each scheme, two power allocation frameworks are formulated each as an optimization problem, where the objective of the former is maximizing the network sum data rate while guaranteeing a certain quality-of-service (QoS) for each user, whereas the goal of the latter is to maximize the minimum data rate among all coexisting users. The formulated optimization problems are not convex, and hence, difficult to be solved directly unless using heuristic methods, which comes at the expense of high computational complexity.

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) employs massive multiple-input multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to the interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the factors such as transmit power and number of scheduled users affect the training loss, the convergence rate of the FL algorithm is first analyzed. Given this analytical result, the user scheduling and energy management optimization problem can be decomposed, simplified, and solved. Further, the system model is extended by considering multiple BSs. Hence, a joint user association and scheduling problem in FL over wireless systems is studied. The optimal user association problem is solved using the branch-and-bound technique. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm., Comment: To appear in IEEE Internet of Things Journal

This paper investigates the channel aging problem of mobile light-fidelity (LiFi) systems. In the LiFi physical layer, the majority of the optimization problems for mobile users are non-convex and require the use of dual decomposition or heuristics techniques. Such techniques are based on iterative algorithms, and often, cause a high processing delay at the physical layer. Hence, the obtained solutions are no longer optimal since the LiFi channels are evolving. In this paper, a proactive-optimization (PO) approach that can alleviate the LiFi channel aging problem is proposed. The core idea is to design a long-short-term-memory (LSTM) network that is capable of predicting posterior positions and orientations of mobile users, which can be then used to predict their channel coefficients. Consequently, the obtained channel coefficients can be exploited to derive near-optimal transmission-schemes prior to the intended service-time, which enables real-time service. Through various simulations, the performance of the designed LSTM model is evaluated in terms of prediction error and time, as well as its application in a practical LiFi optimization problem.

The prevalence of Diabetes mellitus (DM) in the Middle East is exceptionally high as compared to the rest of the world. In fact, the prevalence of diabetes in the Middle East is 17-20%, which is well above the global average of 8-9%. Research has shown that food intake has strong connections with the blood glucose levels of a patient. In this regard, there is a need to build automatic tools to monitor the blood glucose levels of diabetics and their daily food intake. This paper presents an automatic way of tracking continuous glucose and food intake of diabetics using off-the-shelf sensors and machine learning, respectively. Our system not only helps diabetics to track their daily food intake but also assists doctors to analyze the impact of the food in-take on blood glucose in real-time. For food recognition, we collected a large-scale Middle-Eastern food dataset and proposed a fusion-based framework incorporating several existing pre-trained deep models for Middle-Eastern food recognition., Comment: 4 pages

The massive deployment of IoT devices being utilized by home automation, industrial and military scenarios demands for high security and privacy standards to be achieved through innovative solutions. This paper proposes KaFHCa, a crypto-less protocol that generates shared secret keys by combining random frequency hopping collisions and source indistinguishability independently of the radio channel status. While other solutions tie the secret bit rate generation to the current radio channel conditions, thus becoming unpractical in static environments, KaFHCa guarantees almost the same secret bitrate independently of the channel conditions. KaFHCa generates shared secrets through random collisions of the transmitter and the receiver in the radio spectrum, and leverages on the fading phenomena to achieve source indistinguishability, thus preventing unauthorized eavesdroppers from inferring the key. The proposed solution is (almost) independent of the adversary position, works under the conservative assumption of channel fading ({\sigma} = 8dB), and is capable of generating a secret key of 128 bits with less than 564 transmissions.

In vivo wireless medical devices have a critical role in healthcare technologies due to their continuous health monitoring and noninvasive surgery capabilities. In order to fully exploit the potential of such devices, it is necessary to characterize the in vivo wireless communication channel which will help to build reliable and high-performance communication systems. This paper presents preliminary results of experimental characterization for this fascinating communications medium on a human cadaver and compares the results with numerical studies., Comment: 2015 IEEE 82nd Vehicular Technology Conference: VTC2015-Fall