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On Separation of Distributed Estimation and Control for LTI Systems

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On Separation of Distributed Estimation and Control for LTI ...

IEEE Conference on Decision and Control

作者： Anthony J. Savas Shinkyu Park H. Vincent Poor Naomi E. Leonard Department of Mechanical and Aerospace Engineering Princeton University Princeton NJ USA Electrical and Computer Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The separation principle in a centralized estimation and control problem gives us the flexibility to design a feedback controller independent of the state estimator. However, the same principle does not hold when the estimation and control are distributed over a network of agents. In this case, the estimator may need to be redesigned when the controller is revised, which can be computationally expensive. We investigate a weaker notion of the separation principle in the distributed estimation and control of linear time-invariant (LTI) systems. As a main contribution, applying the small-gain theorem, we characterize the notion using matrix inequalities and compute a set of feedback controllers that agents in the network can adopt without redesigning the estimator. We also analyze how the frequency of information exchange between neighboring agents affects the characterization. We illustrate our analytical results through simulations of a multi-vehicle system problem.

关键词： Linear systems Analytical models Computational modeling Estimation Control systems Linear matrix inequalities Adaptive control

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks

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arXiv 2025年

作者： Chen, Jian Lin, Zehui Lin, Wanyu Shi, Wenlong Yin, Xiaoyan Wang, Di Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong Department of Data Science and Artificial Intelligence The Department of Computing The Hong Kong Polytechnic University Hong Kong School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China School of Information Science and Technology Northwest University Xi’an China Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Saudi Arabia

Recently, the practical needs of "the right to be forgotten" in federated learning gave birth to a paradigm known as federated unlearning, which enables the server to forget personal data upon the client’s removal request. Existing studies on federated unlearning have primarily focused on efficiently eliminating the influence of requested data from the client’s model without retraining from scratch, however, they have rarely doubted the reliability of the global model posed by the discrepancy between its prediction performance before and after unlearning. To bridge this gap, we take the first step by introducing a novel malicious unlearning attack dubbed FedMUA, aiming to unveil potential vulnerabilities emerging from federated learning during the unlearning process. Specifically, clients may act as attackers by crafting malicious unlearning requests to manipulate the prediction behavior of the global model. The crux of FedMUA is to mislead the global model into unlearning more information associated with the influential samples for the target sample than anticipated, thus inducing adverse effects on target samples from other clients. To achieve this, we design a novel two-step method, known as Influential Sample Identification and Malicious Unlearning Generation, to identify and subsequently generate malicious feature unlearning requests within the influential samples. By doing so, we can significantly alter the predictions pertaining to the target sample by initiating the malicious feature unlearning requests, leading to the deliberate manipulation for the user adversely. Additionally, we design a new defense mechanism that is highly resilient against malicious unlearning attacks. Extensive experiments on three realistic datasets reveal that FedMUA effectively induces misclassification on target samples and can achieve an 80% attack success rate by triggering only 0.3% malicious unlearning requests. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

Phase-difference-based 3-D Source Localization Using a Compact Receiver Configuration

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Phase-difference-based 3-D Source Localization Using a Compa...

European Signal Processing Conference (EUSIPCO)

作者： Hui Chen Tarig Ballal Tareq Y. Al-Naffouri Computer Electrical and Mathematical Science & Engineering King Abdullah University of Science and Technology (KAUST) Thuwal

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

Source localization has many important applications, especially in tracking and navigation. Trilateration, triangulation, and multilateration are three widely-used techniques for localization depending on the available information. The main drawbacks of these methods are the requirements of a large number of anchors and an elaborately designed layout. To accomplish the localization task with minimal resources while maintaining reasonable accuracy, we propose a 3-D source localization method with a compact infrastructure (prototype realized with 4 anchors located within an area of 2.5×20 cm 2 ) by utilizing only phase-difference information. The proposed method first estimates the direction-of-arrival (DOA) of the target and then finds the candidate 3-D location along on the DOA by minimizing a cost function. This system is compared to the other two similar setups based on simulations and the experimental tests are carried out using acoustic waves. The results show that the proposed approach can achieve 3-D location error of 2.77 cm for a target at 0.5 m without synchronization between the transmitter and the receivers. The relatively small system size and sufficient location accuracy provide possibilities in controller tracking for virtual reality applications.

关键词： Solid modeling Direction-of-arrival estimation Transmitters Receivers Virtual reality Synchronization Task analysis

Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

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arXiv 2024年

作者： Rjoub, Gaith Islam, Saidul Bentahar, Jamal Almaiah, Mohammed Amin Alrawashdeh, Rana Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

GPNST: An Improved Graphical Password Authentication Scheme Leveraging Neural Style Transfer Technique on Smartphones

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International Journal of Information Security 2025年第3期24卷 1-34页

作者： Ray, Palash Giri, Debasis Obaidat, Mohammad S. Jana, Manasi Sasmal, Mahuya Alenazi, Mohammed J. F. Haldia Institute of Technology West Bengal Haldia India Maulana Abul Kalam Azad University of Technology West Bengal Nadia 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 TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India King Saud University Riyadh Saudi Arabia

The growing prevalence of smartphones and the increasing dependence on digital services have made it crucial to have authentication techniques that are both secure and user-friendly. This study examines the effectiveness and usability of graphical passwords named GPNST as an alternative authentication method on mobile devices. Graphical passwords use users’ capacity to remember visuals or patterns, offering a possible equilibrium between security and user pleasure. GPNST is a two-factor authentication technique that requires users to remember content and style images, or stylized images, as portfolio images. Additionally, a unique key token is assigned to each user device and securely sent to the server using the reversible data hiding approach, therefore mitigating several potential attacks. The research uses a combination of quantitative analysis to evaluate the strength of passwords and qualitative evaluations to measure user satisfaction and memorability. We investigate the influence of several design elements in graphical passwords, such as the intricacy of images and the layout of grids, on both the level of security and ease of use. In addition, the study evaluates the robustness of graphical passwords against popular forms of attacks, such as shoulder surfing and brute-force efforts. The findings provide valuable insights regarding the practicality of incorporating graphical passwords into smartphone authentication systems. This has implications for designers, developers, and users who are looking for secure and user-friendly solutions in the constantly changing field of mobile technology. The GPNST scheme demonstrates outstanding performance in our assessment test, with an accuracy rate of up to 94.80% and a login time of 9.61 seconds. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Static random access storage

An open-source, automated, and cost-effective platform for COVID-19 diagnosis and rapid portable genomic surveillance using nanopore sequencing

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Scientific Reports 2023年第1期13卷 1-12页

作者： Ramos-Mandujano, Gerardo Grünberg, Raik Zhang, Yingzi Bi, Chongwei Guzmán-Vega, Francisco J. Shuaib, Muhammad Gorchakov, Rodion V. Xu, Jinna Tehseen, Muhammad Takahashi, Masateru Takahashi, Etsuko Dada, Ashraf Ahmad, Adeel Nazir Hamdan, Samir M. Pain, Arnab Arold, Stefan T. Li, Mo Stem Cell and Regeneration Laboratory Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Structural Biology and Engineering Computational Biology Research Center. Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Pathogen Genomics Laboratory Bioscience Program Biological and Environmental Science and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Health Safety and Environment Department King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Laboratory of DNA Replication and Recombination Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Pathology and Laboratory Medicine King Faisal Specialist Hospital and Research Center Jeddah Saudi Arabia College of Medicine Al Faisal University Riyadh Saudi Arabia KAUST Health King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Bioengineering Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

The COVID-19 pandemic, caused by SARS-CoV-2, has emphasized the necessity for scalable diagnostic workflows using locally produced reagents and basic laboratory equipment with minimal dependence on global supply chains. We introduce an open-source automated platform for high-throughput RNA extraction and pathogen diagnosis, which uses reagents almost entirely produced in-house. This platform integrates our methods for self-manufacturing magnetic nanoparticles and qRT-PCR reagents-both of which have received regulatory approval for clinical use–with an in-house, open-source robotic extraction protocol. It also incorporates our "Nanopore Sequencing of Isothermal Rapid Viral Amplification for Near Real-time Analysis" (NIRVANA) technology, designed for tracking SARS-CoV-2 mutations and variants. The platform exhibits high reproducibility and consistency without cross-contamination, and its limit of detection, sensitivity, and specificity are comparable to commercial assays. Automated NIRVANA effectively identifies circulating SARS-CoV-2 variants. Our in-house, cost-effective reagents, automated diagnostic workflows, and portable genomic surveillance strategies provide a scalable and rapid solution for COVID-19 diagnosis and variant tracking, essential for current and future pandemic responses. © 2023, The Author(s).

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ADAPTIVE MIXED FEM FOR THE STOKES EIGENVALUE PROBLEM

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arXiv 2024年

作者： Boffi, Daniele Khan, Arbaz Computer electrical and mathematical sciences and engineering division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Dipartimento di Matematica "F. Casorati" University of Pavia Via Ferrata 1 Pavia27100 Italy Department of Mathematics Indian Institute of Technology Roorkee Roorkee247667 India

In this paper we discuss the optimal convergence of a standard adaptive scheme based on mixed finite element approximation to the solution of the eigenvalue problem associated with the Stokes equations. The proofs of the quasi-orthogonality and the discrete reliability are presented. Our numerical experiments confirm the efficacy of the proposed adaptive *** Codes 65N30, 65N25, 65N50, 76D0 Copyright © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

Construction of Efficient Authenticated Key Agreement Protocol for Intelligent Transportation System 2

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Construction of Efficient Authenticated Key Agreement Protoc...

2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Nongthombam, Kiranbala Rana, Saurabh Obaidat, Mohammad S. Chhikara, Deepak Mishra, Dheerendra Chandigarh University Department of Mathematics Punjab Mohali140413 India King Abdullah Ii School of Information Technology University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Maulana Azad National Institute of Technology Department of Mathematics / Bioinformatics and Computer Applications Bhopal462003 India Computer Science Department and Director of Cybersecurity Center University of Texas-Permian Basin OdessaTX79762 United States

ISBN: (纸本)9781665464017

To enable intelligent transportation systems, the Internet of Vehicles (IoV) uses wireless communications between roadside units, onboard units), phones, and other gadgets within a vehicle to deliver a new convenient, comfortable, and safe driving experience. IoV is now more exposed and frequently targeted because of its greatly increased reliance on wireless communication, control, and computer technology, To put it another way, an adversary may target such an open channel to intercept, change, inject malicious messages, or erase loaded data. This could lead to replay, impersonation, middle attack, privileged insider, and other related attacks. An authentication protocol should strive to accomplish security, anonymity, and untraceability among other critical aspects. In this research, we suggest a brand-new key agreement and mutual authentication methodology for IoV powered Intelligent transportation system (ITS). We demonstrate that our suggested system is secure against several known threats in IoV- the enabled ITS environment. Furthermore, a thorough comparison of our proposed system to eight other competing ones reveals that it has the lowest communication and computational costs. In addition, it is more secure and provides more functionality characteristics. © 2022 IEEE.

关键词： Authentication

Maritime Communications: A Survey on Enabling Technologies, Opportunities, and Challenges

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arXiv 2022年

作者： Alqurashi, Fahad S. Trichili, Abderrahmen Saeed, Nasir Ooi, Boon S. Alouini, Mohamed-Slim The Computer Electrical and Mathematical Sciences & Engineering King Abdullah University of Science and Technology Makkah Province Thuwal Saudi Arabia Al Ain United Arab Emirates

Water covers 71% of the Earth’s surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and space networks. This paper presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. The paper first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, the paper presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation. © 2022, CC BY.

关键词： Antennas