This paper presents a computationally efficient and distributed speaker diarization framework suitable for a network of IoT-style audio devices. This work proposes a Federated Learning based speaker identification model which can identify the participants in a conversation without the requirement of a large audio database for training. An unsupervised online update mechanism is proposed for the Federated Learning model which depends on cosine similarity of speaker embeddings. Moreover, the proposed diarization system solves the problem of speaker change detection via. unsupervised segmentation techniques using Hotelling's tsquared Statistic and Bayesian Information Criterion. In this new approach, speaker change detection is biased around detected quasi-silences, which reduces the severity of the tradeoff between the missed detection and false detection rates. Additionally, the computational overhead due to frame-byframe identification of speakers is reduced via. unsupervised clustering of speech segments. The results show the effectiveness of the proposed training method in the presence of non-IID speech data. It also shows a considerable improvement in the reduction of false and missed detection at the segmentation stage while reducing the computational overhead. Improved accuracy and reduced computational cost of the proposed mechanism makes it appropriate for real-time speaker diarization, especially for a distributed IoT network.

A document by Jing Nan. Click on the document to view its contents.

Integrated Sensing and Communication (ISAC) is poised to significantly influence sixth-generation (6G) networks by enhancing their ability to intelligently sense and decide. Existing IASC research efforts focuses on radio-frequency (RF) signals, leading to increased strain on the already congested spectrum due to the shared hardware and frequency bands. Given the surge of connected devices and envisaged diverse sensing scenarios, exploring alternate frequency bands becomes crucial. Light Fidelity (LiFi), which utilizes existing lighting infrastructure, stands out as a promising technology due to its highly accurate 3D sensing capabilities. In this paper, we introduce a framework for LiFi-empowered wireless networks, amalgamating illumination, communication, and sensing. We conduct a thorough review of literature on LiFi-based ISAC principles and highlight technologies poised to enhance its performance. We also explore the prospects of multi-modal ISAC by fusing LiFi with other sensory data sources. The paper concludes by outlining prospective research avenues and challenges for efficient LiFi-based ISAC.

Key goal for this study was to conduct a real network traffic sample dataset and did a deep mining to survey for secure the Saudi community by report how the Saudi cyberspace’s pattern is. A kind of a heterogenous simultaneous optical multiprocessor exchange bus architecture used as a backbone network for collecting the network traffic. First, crucial cleaning processes were performed to clean the very noisy and dirty dataset. A total of 1048575 datapoints and 22 features were considered for the model/data evaluation processes. Second, Lazy predict mechanism was recruited to nominate the top-ranking learning models candidates. Third, a powerful supervised computation algorithms used to shape and picture the terra-Byte payload traffic across the Saudi cyber domain. Finally, for choosing the best Saudi cybercrime classification model, an intense digging processes were experimented and analyzed. Performance metrics used are accuracy (Acc), balanced accuracy (BAcc), F1-score, learning time taken, and confusion matrix. Evaluating the performance of different models based on “Destination” as target decision tree classifier (DTC) was the first model (i.e., highest BAcc with low time taken) and Saudi Arabia was the 9th country as a generated source target.  

Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficult to detect. We considered two types of data-poisoning attacks, label flipping (LF) and feature poisoning (FP), and applied them with a novel approach. In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data. For FP, we randomly manipulated the highly contributing features determined using the Random Forest algorithm. The datasets used in this experiment were CIC [1] and UNSW [2] related to computer networks. We generated adversarial samples using the two attacks mentioned above, which were applied to a small percentage of datasets. Subsequently, we trained and tested the accuracy of the model on adversarial datasets. We recorded the results for both benign and manipulated datasets and observed significant differences between the accuracy of the models on different datasets. From the experimental results, it is evident that the LF attack failed, whereas the FP attack showed effective results, which proved its significance in fooling a server. With a 1% LF attack on the CIC, the accuracy was approximately 0.0428 and the ASR was 0.9564; hence, the attack is easily detectable, while with a 1% FP attack, the accuracy and ASR were both approximately 0.9600, hence, FP attacks are difficult to detect. We repeated the experiment with different poisoning percentages.

This paper introduces a preliminary concept aimed at achieving Artificial General Intelligence (AGI) by leveraging a novel approach rooted in two key aspects. Firstly, we present the General Intelligent Network (GIN) paradigm, which integrates information entropy principles with a generative network, reminiscent of Generative Adversarial Networks (GANs). Within the GIN network, original multimodal information is encoded as low information entropy hidden state representations (HPPs). These HPPs serve as efficient carriers of contextual information, enabling reverse parsing by contextually relevant generative networks to reconstruct observable information. Secondly, we propose a Generalized Machine Learning Operating System (GML System) to facilitate the seamless integration of the GIN paradigm into the AGI framework. The GML system comprises three fundamental components: an Observable Processor (AOP) responsible for real-time processing of observable information, an HPP Storage System for the efficient retention of low entropy hidden state representations, and a Multimodal Implicit Sensing/Execution Network designed to handle diverse sensory inputs and execute corresponding actions. By combining the GIN paradigm and GML system, our approach aims to create a holistic AGI system capable of encoding, processing, and reconstructing information in a manner akin to human-like intelligence. The synergy of information entropy principles and generative networks, along with the orchestrated functioning of the GML system, presents a promising avenue towards achieving advanced cognitive capabilities in artificial systems. This preliminary concept lays the groundwork for further exploration and refinement in the pursuit of true brain-like intelligence in machines.

The purpose of this study is to investigate and comprehend the performance analysis of a compact planar multiband multiple-input-multiple-output (MIMO) antenna, accomplished as a part of the ECE 533: Advanced Antenna Design course at Washington State University, Vancouver during the Fall 2022 semester. This study has introduced two symmetrical radiating elements joined by a neutralizing line to nullify the reactive coupling that makes up the MIMO antenna's basic structure. The basic MIMO antenna occupies an overall three dimensions of 60x80x0.8mm3 volume on a FR4 substrate with relative permittivity er=4.40 and loss tangent of tanδ=0.02. Coplanar waveguide (CPW) transmission lines of 50Ω have been used to feed the MIMO antenna. Furthermore, the base plane of the basic MIMO antenna has four slits and two compact rectangles of 2x12mm2 cut into it to compensate the mutual coupling. The slit width, neutralizing line, substrate material, and its thickness have all been tuned in the proposed techniques to analyze different antenna parameters. The operating frequency band has been set to 500MHz-3500MHz for all the four cases. By employing simulation results obtained from the ANSYS HFSS environment, the performance of the fundamental MIMO antenna is evaluated and assessed against the optimized models. The optimized versions of the conventional MIMO antenna design have been thoroughly discussed in separate case studies. From our software simulation analysis, we find that optimized geometric shapes of the compact planar MIMO antenna show significant improvement in the isolation parameter of |S21|, from <= 14.9 dB to <= 18.44 dB, <= 20 dB and up to <=27.68 dB for ease of understanding, no servicing frequencies have been predetermined.

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.

In the era of 5G and Beyond (5G+) networks, characterized by increased complexity and vulnerability to cyberthreats, the detection of cyberattacks within network traffic becomes more challenging. Machine Learning (ML) offers a promising solution for detecting cyberthreats. However, the constantly ever-evolving technology landscape introduces rapidly evolving attacks, requiring continuous ML model updates. Accordingly, this paper leverages the power of Automated ML (AutoML) and Digital Twin (DT) technologies to deploy an Intrusion Detection System (IDS) in resource-constrained environments, which remains effective over time. An AutoML pipeline is proposed in this paper for multi-class network attack detection, consisting of three offline and automated phases-data preprocessing, feature engineering, and model learning-and an online phase for model monitoring and updates. Additionally, a DT has been introduced to continually update and evolve the ML model in response to the dynamic nature of new attacks, emphasizing low overhead and efficient synchronization. Specifically, two data generation approaches within the DT are explored: uniform sampling based on statistical properties and generative models (such as Variational AutoEncoders (VAEs) and Generative Adversarial Networks (GANs)) using raw data. The experimental results demonstrate that uniform sampling achieves the fastest recovery, lowest overhead, and highest privacy in enhancing the multi-layer perceptron, the best-performing ML model.

In recent times, there has been a growing interest in the wireless community regarding the integration of intelligent reflecting surface (IRS) assistance in millimeter-wave (mmWave) and terahertz (THz) communications. This research paper sets out to develop a beam-centric multiple-access strategy tailored for this emerging paradigm. The fundamental concept involves employing multiple sub-arrays within a hybrid digital-analog array to create distinct beams. Each beam is then directed independently towards the desired direction, effectively mitigating inter-user interference and suppressing undesired signal reflections. The proposed approach amalgamates the benefits of both orthogonal multiple access (ensuring no inter-user interference) and non-orthogonal multiple access (utilizing the full time-frequency resource spectrum). As a result, this strategy has the potential to significantly enhance system capacity, a conclusion supported by Monte-Carlo simulations.

Bit and power loading (BPL) algorithms played a pivotal role in the success of orthogonal frequency division multiplexing (OFDM) in digital transmission, including lightemitting diode (LED) based wireless optical communications. Nevertheless, the conventional BPL algorithms do not distinguish the nonlinear distortion generated in LED transmitters from an additive noise, which leaves room for improvement. This letter presents a novel power loading and two BPL algorithms that maximize the transmission capacity while minimizing the nonlinear distortion generated in LED. The effectiveness of the proposed algorithms is evaluated through simulations and transmission experiments, revealing a throughput increase of up to 10% in comparison to what can be achieved employing classical algorithms.

In the field of dentistry, there is a growing demand for increased precision in diagnostic tools, with a specific focus on advanced imaging techniques such as computed tomography, cone beam computed tomography, magnetic resonance imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep learning has emerged as a pivotal tool in this context, enabling the implementation of automated segmentation techniques crucial for extracting essential diagnostic data. This integration of cutting-edge technology addresses the urgent need for effective management of dental conditions, which, if left undetected, can have a significant impact on human health. The impressive track record of deep learning across various domains, including dentistry, underscores its potential to revolutionize early detection and treatment of oral health issues. Objective: Having demonstrated significant results in diagnosis and prediction, deep convolutional neural networks (CNNs) represent an emerging field of multidisciplinary research. The goals of this study were to provide a concise overview of the state of the art, standardize the current debate, and establish baselines for future research. Method: In this study, a systematic literature review is employed as a methodology to identify and select relevant studies that specifically investigate the deep learning technique for dental imaging analysis. This study elucidates the methodological approach, including the systematic collection of data, statistical analysis, and subsequent dissemination of outcomes. Results: In incorporating 45 studies, we identified selection criteria and research objectives, addressing significant gaps in the existing literature. These studies assist clinicians in examining dental conditions and classifying dental structures, including caries detection and the identification of various tooth types. We evaluated model performance, addressing the identified gaps, using diverse metrics that we strive to list and explain. Conclusion: This work demonstrates how Convolutional Neural Networks (CNNs) can be employed to analyze images, serving as effective tools for detecting dental pathologies. Although this research acknowledged some limitations, CNNs utilized for segmenting and categorizing teeth exhibited their highest level of performance overall.

In this paper, we consider an unmanned aerial vehicle (UAV) assisted communications system, including two cooperative UAVs, a wireless-powered ground destination node leveraging simultaneous wireless information and power transfer (SWIPT) technique, and a terrestrial passive eavesdropper. One UAV delivers confidential information to destination and the other sends jamming signals to against eavesdropping and assist destination with energy harvesting. Assuming UAVs have partial information about eavesdropper’s location, we propose two transmission schemes: friendly UAV jamming (FUJ) and Gaussian jamming transmission (GJT) for the cases when jamming signals are known and unknown a priori at destination, respectively. Then, we formulate an average secrecy rate maximization problem to jointly optimize the transmission power and trajectory of UAVs, and the power splitting ratio of destination. Being non-convex and hence difficult to solve the formulated problem, we propose a computationally efficient iterative algorithm based on block coordinate descent and successive convex approximation to obtain a suboptimal solution. Finally, numerical results are provided to substantiate the effectiveness of our proposed multiple-UAV schemes, compared to other existing benchmarks. Specifically, we find that the FUJ demonstrates significant secrecy performance improvement in terms of the optimal instantaneous and average secrecy rate compared to the GJT and the conventional single-UAV counterpart.

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets from a remote IoT device to its receiver in two hops with strict latency requirements, in the presence of an eavesdropper. This system requires careful optimization of important design parameters, such as the coding blocklengths of both hops, transmit powers, and the UAV’s trajectory. While the overall optimization problem is nonconvex, we tackle it by applying a block successive convex approximation (BSCA) approach to divide the original problem into three subproblems and solve them separately. Then, an overall iterative algorithm is proposed to obtain the final design with guaranteed convergence. Our proposed low-complexity algorithm incorporates robust trajectory design and resource management to optimize the effective average secrecy throughput of the communication system over the course of the UAV-relay’s mission. Simulation results demonstrate significant performance improvements compared to various benchmark schemes and provide useful design insights on the coding blocklengths and transmit powers along the trajectory of the UAV.

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure − secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks.

Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on highrate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There's a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88%. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks.

Sustainable resource management within a cloud computing environment is a highly critical and prominently studied research topic. In this context, this paper presented a comprehensive survey of potential sustainable resource management (Sus-RM) strategies that have addressed the energy optimization challenges during cloud workload scheduling and resource management. The perspective of sustainable cloud resource management followed by a discussion of intended motivation, challenges, objectives, and approaches is manifested. The designed research methodology with the proposed method-centric classification and taxonomy of Sus-RM approaches is conferred. Based on the common features of managing sustainability dealing with common operations including task scheduling, virtual machine (VM) placement, and VM rescheduling or migration, the Sus-RM strategies are further grouped into a common class or category. The concept behind each methodbased approach and respective state-of-the-art strategies belonging to each category are concisely discussed with their pandect comparative summary. Besides, conceptual and theoretical analysis, the critical takeaways and lessons learned outlining each method are presented. Further, the trade-off among the leading strategies is capsuled and discussed respectively to their critical features to put forward imperative concluding remarks about the holistic study of cloud Sus-RM. Finally, the scientific survey study is concluded with insightful and concrete future research directions.

Light-emitting diodes (LEDs) are widely used in visible light communication (VLC) systems, but their bandwidth is generally incomparable to the laser diodes (LDs). Though LDs are exploited in free space optical systems to provide high data rate, such systems generally fail for mobile users due to the challenges in acquisition, tracking and pointing. Recently, various centimeter-level visible light positioning (VLP) systems have been realized, making it possible to realize VLP-assisted communication systems. In this work, we design a novel VLP-assisted indoor laser communication system, where an LED provides the positioning signal for the mobile user and a narrow-beam laser carries high-speed data stream. The estimated position is updated regularly to continuously orient the laser boresight to the user. The error of the VLP system and the outage probability of the VLP-assisted laser communication system are both derived in closed forms. According to the analytical and simulation results, the outage probability is related to the changes of the system parameters, such as the transmission power of the laser and the user position. Moreover, the outage probability can be minimized by optimizing the laser's beam divergence angle. An experimental platform is built to verify the feasibility of our proposed system.

Reconfigurable intelligent surfaces (RISs) operate similarly to electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an applicable EM environment allowing for flexible adaptation and fostering sustainability in terms of economic deployment and energy efficiency. However, the conventional RIS is controlled through high-latency field programmable gate array or micro-controller circuits usually implementing artificial neural networks (ANNs) for tuning the RIS phase array that have also very high energy requirements. Most importantly, conventional RIS are unable to function under realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this paper, we benefit from the advanced computing power of neuromorphic processors and design a new type of RIS named NeuroRIS, to supporting high mobility UEs through real time adaptation to the ever-changing wireless channel conditions. To this end, the neuromorphic processing unit tunes all the RIS metaelements in the orders of ns for particular switching circuits e.g., varactors while exhibiting significantly low energy requirements since it is based on event-driven processing through spiking neural networks for accurate and efficient phase-shift vector design. Numerical results show that the NeuroRIS achieves very close rate performance to a conventional RIS-based on ANNs, while requiring significantly reduced energy consumption with the latter.

Common block ciphers like AES specified by the NIST or KASUMI (A5/3) of GSM are extensively utilized by billions of individuals globally to protect their privacy and maintain confidentiality in daily communications. However, these ciphers lack comprehensive security proofs against the vast majority of known attacks. Currently, security proofs are limited to differential and linear attacks for both AES and KASUMI. For instance, the consensus on the security of AES is not based on formal mathematical proofs but on intensive cryptanalysis over its reduced rounds spanning several decades. In this work, we introduce new security proofs for AES against another attack method: impossible differential (ID) attacks. We classify ID attacks as reciprocal and nonreciprocal ID attacks. We show that sharp and generic lower bounds can be imposed on the data complexities of reciprocal ID attacks on substitution permutation networks. We prove that the minimum data required for a reciprocal ID attack on AES using a conventional ID characteristic is 2 66 chosen plaintexts whereas a nonreciprocal ID attack involves at least 2 88 computational steps. We mount a nonreciprocal ID attack on 6-round AES for 192-bit and 256-bit keys, which requires only 2 18 chosen plaintexts and outperforms the data complexity of any attack. Given its marginal time complexity, this attack does not pose a substantial threat to the security of AES. However, we have made enhancements to the integral attack on 6-round AES, thereby surpassing the longstanding record for the most efficient attack after a period of 23 years.

This paper proposes block-level precoder (BLP) designs for a multi-input single-output (MISO) system that performs joint sensing and communication across multiple cells and users. The Cramer-Rao-Bound for estimating a target's azimuth angle is determined for coordinated beamforming (CBF) and coordinated multi-point (CoMP) scenarios while considering inter-cell communication and sensing links. The formulated optimization problems to minimize the CRB and maximize the minimum-signal-to-interference-plus-noise-ratio (SINR) are nonconvex and are represented in the semidefinite relaxed (SDR) form to solve using an alternate optimization algorithm. The proposed solutions show improved performance compared to the baseline scenario that neglects the signal component from neighboring cells.

Intelligent omni-surfaces (IOSs) have been considered for assisting outdoor-to-indoor millimeter-wave (mmWave) communications. Nevertheless, the existing works have not adequately investigated how the number or the deployment locations of IOSs should be optimized for serving multiple indoor users. In this paper, we study IOS-assisted outdoor-to-indoor mmWave communications where IOSs are installed in an exterior wall of a building to refract mmWave signals from an outdoor base station (BS) to indoor users that locate among indoor blockages. Given a fixed total number of refracting elements, we formulate an optimization problem to maximize the downlink energy efficiency of the outdoor BS while satisfying the dowlink data rate requirements of the indoor users by jointly optimizing the number, locations and phase shifts of IOSs and the beamforming vectors of the BS. To address the varying dimensionality and the non-convexity of the optimization problem, we decompose it into two subproblems that optimize the IOSs' phase shifts together with the BS beamforming vectors and the number and locations of IOSs, respectively, and devise successive convex approximation and Continuous Population-Based Incremental Learning-based algorithms to solve them alternately. Simulation results demonstrate that the proposed algorithms can obtain the optimal number and locations of IOSs, resulting in significantly enhanced energy efficiency of the outdoor BS compared to benchmark schemes.

A new approach is developed for the joint queue-length distribution of the two-level non-preemptive Markovian priority queue that allows explicit and exact results to be obtained. Marginal distributions are derived for the general multi-level problem. The results are based on a representation of the joint queue-length probability mass function as a single-variable complex contour integral, that reduces to a real integral on a finite interval arising from a cut on the real axis. Both numerical quadrature rules and exact finite sums, involving Legendre polynomials and their generalization, are presented for the joint and marginal distributions. A high level of accuracy is demonstrated across the entire ergodic region. Relationships are established with the waiting-time distributions. Asymptotic behaviour in the large queue-length regime is extracted.