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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Payel Biswas Debashis De Mohammad S. Obaidat Arnab Hazra Balqies Sadoun Department of Computer Science Jogesh Chandra Chaudhuri College Kolkata India Department of Computer Science and Engineering Centre of Mobile Cloud Computing Maulana Abul Kalam Azad University of Technology Nadia WB West Bengal India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar Kattankulathur TN India School of Engineering The Amity University Noida UP India Department of Computer Science and Engineering Dr. Sudhir Chandra Sur Institute of Technology & Sports Complex Kolkata India College of Engineering Al Balqa ‘ Applied University Al-Salt Jordan

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT -mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-time data. The result shows FedHZone with stacked LSTM having “'2%-7% better accuracy than the vanilla LSTM-based model.

关键词： Training Federated learning Atmospheric modeling Time series analysis Real-time systems Internet Telecommunication computing Servers Long short term memory Drones

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On the Low-Frequency Scaling of Vector Potential Integral Equation Solutions

On the Low-Frequency Scaling of Vector Potential Integral Eq...

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Antennas and Propagation Society International Symposium

作者： Rui Chen Hakan Bagci Division of Computer Electrical and Mathematical Science and Engineering (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

ISBN: (纸本)9781728146713

Vector potential integral equations (VPIEs) have recently been proposed as break-down free alternatives to field integral equations for analyzing low-frequency electromagnetic scattering problems. In this work, accuracy of VPIEs for perfect electrically conducting scatterers at low frequencies is investigated. Depending on the different representation of the vector potential formulation, four different integral equations in unknown electric current and normal component of the vector potential are obtained. To numerically solve these VPIEs, the electric current and the normal component of the vector potential are expanded using Rao-Wilton-Glisson and pulse basis functions, respectively. Inserting these expansions into two of the integral equations and using Galerkin testing yield a linear matrix system. Numerical results show that depending on the choice of VPIEs, the solution of this linear matrix system is inaccurate at low frequencies, similar to the magnetic field integral equation.

关键词： Electric potential Integral equations Meetings Electromagnetic scattering Magnetic fields Current Method of moments

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Water Quality Index (WQI) Prediction Using Machine Learning Algorithms

Water Quality Index (WQI) Prediction Using Machine Learning ...

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Kunyanuth Kularbphettong Phanu Waraporn Nareenart Raksuntorn Rujijan Vivhivanives Chanyapat Sangsuwon Chongrag Boonseng Computer Science Program Suan Sunandha Rajabhat University Bangkok Thailand Robotics Engineering Program Suan Sunandha Rajabhat University Bangkok Thailand Faculty of Science and Technology Suan Sunandha Rajabhat Universit Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.

关键词： Water resources Water quality Indexes Classification algorithms Support vector machines Linear regression Climate change Predictive models Environmental monitoring Quality assessment Surface treatment

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L-SVRG and L-Katyusha with arbitrary sampling

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 4991-5039页

作者： Xun Qian Zheng Qu Peter Richtárik Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Mathematics The University of Hong Kong Hong Kong

We develop and analyze a new family of nonaccelerated and accelerated loopless variance-reduced methods for finite-sum optimization problems. Our convergence analysis relies on a novel expected smoothness condition which upper bounds the variance of the stochastic gradient estimation by a constant times a distance-like function. This allows us to handle with ease arbitrary sampling schemes as well as the nonconvex case. We perform an indepth estimation of these expected smoothness parameters and propose new importance samplings which allow linear speedup when the expected minibatch size is in a certain range. Furthermore, a connection between these expected smoothness parameters and expected separable overapproximation (ESO) is established, which allows us to exploit data sparsity as well. Our general methods and results recover as special cases the loopless SVRG (Hofmann et al., 2015) and loopless Katyusha (Kovalev et al., 2019) methods.

关键词： L-SVRG L-Katyusha arbitrary sampling expected smoothness ESO

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M- VMT-PC: Metaverse-Enabled Virtual Medical Training and Patient Care Framework Underlying 5G

M- VMT-PC: Metaverse-Enabled Virtual Medical Training and Pa...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Aparna Kumari Mohammad Obaidat Shikhar Panchal Nikhil Raval Prasun Kumar Dileep Kumar Yadav Department of CSE Nirma University Ahmedabad India King Abdullah II School of Information Technology The University of Jordan Amman University of Science and Technology Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar Kattankulathur TN India School of Engineering The Amity University Noida UP India Department of Computer Science and Engineering Nirma University Ahmedabad India Department of Biotechnology SSET Sharda University Greater Noida India Department of Computer Science and Engineering SCSET Bennett University Greater Noida

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

As technology advances, merging virtual environments, blockchain, and 5G networks opens new possibilities, especially in medical training. This paper introduces a framework that combines these technologies to create an innovative medical training system. In this paper, we proposes Metaverse-enabled Virtual Medical Training and Patient Care framework, i.e., M- VMT-PC. In the proposed framework, metaverse comprises a dig-ital space where users can interact in 3D, offers exciting opportunities for medical training. It allows for realistic and interactive simulations, where trainees can practice medical scenarios and procedures in a virtual setting. More, the M- VMT-PC uses blockchain technology within this framework to ensure that the data is securely stored, enhancing its reliability. Next, 5G technology is used to make this framework effective. With ultra-reliable, low-latency communication (uRLLC) and high-speed data transfer (eMBB), 5G supports real-time interactions and smoothly handles large volumes of data. This technology ensures the training environment is responsive and can handle extensive data needs, essential for ongoing updates and improvements. Our proposed framework M- VMT-PC addresses the current challenges in medical training by providing a scalable, secure, and engaging learning environment. It improves the medical professionals training and sets the stage for future advancements in medical education and patient care.

关键词： Training Three-dimensional displays 5G mobile communication Metaverse Merging Ultra reliable low latency communication Real-time systems Blockchains Telecommunication computing Reliability

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Robust network formation with biological applications

arXiv

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arXiv 2023年

作者： Haskovec, Jan Vybíral, Jan Mathematical and Computer Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Mathematics Faculty of Nuclear Sciences and Physical Engineering Czech Technical University Trojanova 12 Praha12000 Czech Republic

We provide new results on the structure of optimal transportation networks obtained as minimizers of an energy cost functional consisting of a kinetic (pumping) and material (metabolic) cost terms, constrained by a local mass conservation law. In particular, we prove that every tree (i.e., graph without loops) represents a local minimizer of the energy with concave metabolic cost. For the linear metabolic cost, we prove that the set of minimizers contains a loop-free structure. Moreover, we enrich the energy functional such that it accounts also for robustness of the network, measured in terms of the Fiedler number of the graph with edge weights given by their conductivities. We examine fundamental properties of the modified functional, in particular, its convexity and differentiability. We provide analytical insights into the new model by considering two simple examples. Subsequently, we employ the projected subgradient method to find global minimizers of the modified functional numerically. We then present two numerical examples, illustrating how the optimal graph’s structure and energy expenditure depend on the required robustness of the network. © 2023, CC BY-NC-ND.

关键词： Metabolism

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Towards Sustainable Internet of Underwater Things: UAV-aided Energy Efficient Wake-up Solutions

arXiv

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arXiv 2022年

作者： Muzzammil, Muhammad Kouzayha, Nour Saeed, Nasir Al-Naffouri, Tareq Y. College of Underwater Acoustic Engineering Harbin Engineering University China Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Remote Sensing Unit Department of Electrical Engineering Northern Border University Arar Saudi Arabia

With the advancements in underwater wireless communications, internet of underwater things (IoUT) realization is inevitable to enable many practical applications, such as exploring ocean resources, ocean monitoring, underwater navigation, and surveillance. The IoUT network comprises battery-operated sensor nodes, and replacing or charging such batteries is challenging due to the harsh ocean environment. Hence, an energy-efficient IoUT network development becomes vital to improve the network lifetime. Therefore, this paper proposes unmanned aerial vehicle (UAV)-aided energy-efficient wake-up designs to activate the underwater IoT nodes on-demand and reduce their energy consumption. Specifically, the UAV communicates with water surface nodes, i.e., buoys, to send wake-up signals to activate the IoUT sensor nodes from sleep mode. We present three different technologies to enable underwater wake-up: acoustic, optical, and magnetic induction-based solutions. Moreover, we verify the significance of each technology through simulations using the performance metrics of received power and lifetime. Also, the results of the proposed on-demand wake-up approach are compared to conventional duty cycling, showing the superior performance of the proposed schemes. Finally, we present some exciting research challenges and future directions. © 2022, CC BY.

关键词： Sensor nodes

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Unbiased estimation of the gradient of the log-likelihood for a class of continuous-time state-space models

arXiv

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arXiv 2021年

作者： Ballesio, Marco Jasra, Ajay Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper, we consider static parameter estimation for a class of continuous-time state-space models. Our goal is to obtain an unbiased estimate of the gradient of the log-likelihood (score function), which is an estimate that is unbiased even if the stochastic processes involved in the model must be discretized in time. To achieve this goal, we apply a doubly randomized scheme (see, e.g., [13, 14]), that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization [15]. Our novel estimate helps facilitate the application of gradient-based estimation algorithms, such as stochastic-gradient Langevin descent. We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee & Glynn estimator [22, 23]. © 2021, CC BY.

关键词： Monte Carlo methods

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Low-coherence semiconductor light sources: devices and applications

npj Nanophotonics

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npj Nanophotonics 2024年第1期1卷 1-19页

作者： Hang Lu Omar Alkhazragi Yue Wang Nawal Almaymoni Wenbo Yan Wahyu Hendra Gunawan Heming Lin Tae-Yong Park Tien Khee Ng Boon S. Ooi Photonics Laboratory Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Sciences and Engineering (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia

Since the invention of the laser, there have been countless applications that were made possible or improved through exploiting its multitude of unique advantages. Most of these advantages are mainly due to the high degree of coherence of the laser light, which makes it directional and spectrally pure. Nevertheless, many fields require a moderate degree of temporal or spatial coherence, making conventional lasers unsuitable for these applications. This has brought about a great interest in partially coherent light sources, especially those based on semiconductor devices, given their efficiency, compactness, and high-speed operation. Here, we review the development of low-coherence semiconductor light sources, including superluminescent diodes, highly multimode lasers, and random lasers, and the wide range of applications in which they have been deployed. We highlight how each of these applications benefsits from a lower degree of coherence in space and/or time. We then discuss future potential applications that can be enabled using new types of low-coherence light.

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