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Securing Mobile Robotic Networks Against Replacement Attack

IEEE Transactions on Automatic Control 2025年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Cai, Lin Shanghai Jiao Tong University Dept. of Automation China Ministry of Education of China The Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China University of Victoria Dept. of Electrical and Computer Engineering BC Canada

The security of mobile robotic networks (MRNs) has been an active research topic in recent years. This paper aims to secure the ubiquitous formation control of MRNs against the replacement attack, where an external robot can replace a formation robot by compromising the communication and physically interfering with the victim simultaneously. To counter this advanced attack, the novel idea of this work is to leverage the physical proximity of the formation shape and the interaction topology among robots for defense design. First, from the physical proximity perspective, we propose the convex neighbor polygon (CNP) to capture the geometric characteristic of the formation robots, and design a CNP-based security mechanism for the robots during the replacement attack. Then, from the interaction perspective, we introduce the indirect controllability to characterize the possibility that the attacker leverages the interaction between robots to deactivate the CNP mechanism, and establish the conditions regarding the topology structure and the attack input to counter the replacement. Finally, based on the obtained conditions, we demonstrate how to design the initiatory topology among the formation robots to enhance the defense performance. Comparative simulations verify the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Topology

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Efficient Resource Allocation in Vehicular Networks Using Embedding-Enhanced MAPPO

Efficient Resource Allocation in Vehicular Networks Using Em...

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Smart World Congress (SWC), IEEE

作者： Wen Qiu Xun Shao Wei Zhao Hiroshi Masui Information and Communication Engineering Kitami Institute of Technology Hokkaido Japan Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Toyohashi Japan School of Computer Science and Technology Anhui University of Technology Anhui China Information Processing Center Kitami Institute of Technology Hokkaido Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

With the swift advancement of automotive technology, the need for efficient allocation of resources in V2V (vehicle-to-vehicle) communications has grown significantly. However, the dynamic nature of channels in vehicular networks introduces considerable uncertainty in resource allocation. This research presents a novel approach to addressing these challenges by utilizing multi-agent deep reinforcement learning (MADRL). By leveraging shared resource blocks (RBs) for V2V and vehicle-to-infrastructure (V2I) communications, our approach minimizes interference with V2I links and ensures optimal V2V performance. Unlike traditional methods that select actions sequentially, our approach treats every V2V link as an agent and simultaneously determines all agents’ actions to improve coordination and efficiency. To accelerate the training of the multi-agent critic network, we integrate an embedding module to simplify and compress the network input dimensions. Through the use of our embedding multi-agent proximal policy optimization (E-MAPPO) algorithm, the agents collaboratively optimize their transmission strategies to efficiently share spectrum with V2I links. Simulation results show that our method greatly minimizes interference with V2I links while meeting the stringent latency and reliability requirements of V 2 V communications, surpassing baseline approaches.

关键词： Training Uncertainty Simulation Vehicle-to-infrastructure Scalability Vehicular ad hoc networks Interference Deep reinforcement learning Resource management Vehicle dynamics

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Speech Emotion Recognition Using yet Another Mobile Network Tool

Speech Emotion Recognition Using yet Another Mobile Network ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Oyediran, Mayowa Oyedepo Ojo, Olufemi Samuel Bharany, Salil Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Farhaoui, Yousef Awotunde, Joseph Bamidele Department of Computer Science Ajayi Crowther University Oyo Oyo Nigeria CET Department GNDU Amritsar India Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria

ISBN: (纸本)9783031262531

Recognizing and reacting to the emotions of others is a key necessity for successful human engagement since emotions are such an important and vital component of being human. Speaking is the most intuitive method by which individuals ex-press themselves. It follows that this communication medium should be extended to computer programs. Affective computing focuses on the automated perception, understanding, and synthesizing of emotional responses, consequently, speech emotion recognition seeks to recognize a speaker's core emotional state derived from the voice signal. The paralinguistic information conveyed by speech expressions has been identified to be beneficial in speech processing in a variety of ways, most notably as a significant component of computer emotional intelligence and contributing to Human – computer Interaction. In this regard, this study uses a spoken emotion recognition technology to deliver "Yet Another Mobile Net-work Tool" where 1295 primary dataset acquired from Bells University of Technology. The dataset consists of five emotions (anger, fear, neutral, happy, and sad) that were used for the experiment. The experimental results show that the study obtained a recognition accuracy of 85.5% and a rejection rate of 10.5%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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When Task Scheduling Meets Flexible-bandwidth Optical Interconnects: A Cross-layer Resource Orchestration Design

When Task Scheduling Meets Flexible-bandwidth Optical Interc...

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2022 Optical Fiber Communications Conference and Exhibition, OFC 2022

作者： Chen, Xiaoliang Liu, Che-Yu Proietti, Roberto Chen, Shaoyi Li, Zhaohui Yoo, S.J. Ben Sun Yat-sen University Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China University of California Department of Computer Science DavisCA95616 United States University of California Department of Electrical and Computer Engineering DavisCA95616 United States

ISBN: (纸本)9781557524669

We propose a cross-layer resource orchestration design for task scheduling in flexible-bandwidth optical data center networks. Results show the proposed design can achieve 8.2 ×, 1.9 × and 4.8 × reductions of request blocking probability, end-to-end delay and packet loss rate, compared with the baseline. © 2022 The Author(s)

关键词： Blocking probability

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Keynote Talk: Qualitative model based diagnosis of complex systems using decomposition 22

Keynote Talk: Qualitative model based diagnosis of complex s...

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Proceedings of the 7th International Conference on Sustainable information engineering and Technology

作者： Katalin M. Hangos Systems and Control Laboratory Institute for Computer Science and Control Hungary and Department of Electrical Engineering and Information Systems University of Pannonia Hungary

ISBN: (纸本)9781450397117

Decomposition offers the potential to reduce the complexity of model-based optimization, prediction, control and diagnosis by accounting for the structure and sparsity of the describing model. Motivated by this fact, a rich and powerful collection of decomposition methods are available for model based diagnosis of large-scale complex dynamic systems, too. At the same time, one usually does not have enough information about a large-scale complex dynamic system to construct its precise enough model, so a kind of qualitative dynamic model is often used for the diagnosis [1]. Two structural decomposition based qualitative diagnostic methods are presented in this lecture, together with their component-driven system decomposition ***, a model-based diagnostic method is described that is able to detect and isolate non-technical losses (illegal loads) in low voltage electrical grids of one transformer area [2]. As a preliminary off-line step of the diagnosis, a powerful electrical decomposition method is proposed, which breaks down the overall network to subsystems with one feeder layout enabling to make the necessary computations efficient. The diagnostic method is based on analyzing the differences between the measured and model-predicted voltages. The uncertainty in the model parameters together with the measurement uncertainties are also taken into account to make the approach applicable in real-world cases. The proposed method is able to detect and localize multiple illegal loads, and the amount of the illegal consumption can also be *** a second case study, a high level decomposition approach for process system fault diagnosis using event traces is given [3], [4]. Using a simple component graph model behind the process system and the measured trace applied for the diagnosis, the method can find the root cause(s) of propagating failures between separate components. The method can connect individually operating lower-level component-specific diag

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Communication-efficient and Decentralized Federated Minimax Optimization

Communication-efficient and Decentralized Federated Minimax ...

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Annual Allerton Conference on Communication, Control, and Computing

作者： Sajjad Ghiasvand Amirhossein Reisizadeh Mahnoosh Alizadeh Ramtin Pedarsani Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

As distributed learning applications like Federated Learning, the Internet of Things (IoT), and Edge Computing expand, addressing their limitations becomes crucial. We approach decentralized learning across a network of communicating clients or nodes, focusing on two primary challenges: data heterogeneity and adversarial robustness. To address these, we introduce a decentralized minimax optimization method incorporating two key components: local updates and gradient tracking. Minimax optimization serves as a fundamental tool for adversarial training, ensuring robustness. Local updates are vital in Federated Learning (FL) to alleviate the communication bottleneck, while gradient tracking is necessary to demonstrate convergence amid data heterogeneity. Our analysis of the proposed algorithm, Dec-Fed Track, in nonconvex-strongly-concave minimax optimization demonstrates its convergence to a stationary point. Additionally, numerical experiments support our theoretical results.

关键词： Training computer aided instruction Federated learning Distance learning Optimization methods Focusing Robustness Internet of Things Convergence Edge computing

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Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Domain Power Allocation 15

Maximizing Energy Efficiency of HSRC: A Cache-Aided Time-Dom...

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15th IEEE International Conference on Wireless Communications and Signal processing, WCSP 2023

作者： Li, Yeshen Xiong, Ke Li, Han Ai, Bo Fan, Pingyi Letaief, Khaled Ben Beijing Jiaotong University Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing100044 China Beijing Jiaotong University Collaborative Innovation Center of Railway Traffic Safety Beijing100044 China Beijing Jiaotong University State Key Laboratory of Electronic and Information Engineering School Beijing100044 China Tsinghua University Department of Electronic Engineering Beijing100084 China Department of Electrical and Computer Engineering Hong Kong

ISBN: (纸本)9798350324662

This paper presents a cache-aided time-domain power allocation (CaTDPA) for 5G high-speed railway communications (HSRC), where two kinds of users, online interactive application users (OIUs) and online non-interactive application users (ONIUs), are jointly taken into account. For OIUs, their minimum ground-train data rate demand is guaranteed. For ONIUs, their minimum cache-releasing data rate demand is guaranteed. To achieve the green HSRC design, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the power allocation (PA) and cache releasing start time (CRST) subject to the constraints of total available energy, maximum transmit power, ONIUs' ground-train data rate threshold, ONIUs' cache-releasing data rate threshold, and causality of CRST. To address such a non-convex and fractional problem, a two-step solution is designed. First, with a given CRST, a Dinkelbach-based method is applied to obtain the correspondingly optimal PA. Second, the one-dimensional traversal method is used to find the optimal CRST. Experimental results show that our proposed CaTDPA achieves the maximum EE among multiple compared methods while meeting the requirements of OIUs and ONIUs. Moreover, EE will decrease as the OIUs' ground-train data rate threshold and the train's moving speed increase, while it is observed that the ONIUs' cache-releasing data rate threshold has a relatively small effect on the EE of HSRC. © 2023 IEEE.

关键词： cache Energy efficiency high-speed railway power allocation

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning 13

Quality Assessment of Electrocardiogram Signals Using Contra...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Zhong, Meiping Li, Zuoyong Wang, ChuanSheng Huang, Jiayan Zhao, WanQi Fan, Haoyi College of Computer and Big Data Fuzhou University Fuzhou350108 China College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China Polytechnic University of Catalonia Department of Automatic Control Technical Barcelona08034 Spain New Engineering Industry College Putian University Putian351100 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9798350319156

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods. © 2023 IEEE.

关键词： Electrocardiograms

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An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xu, Saijuan Yang, Liliang Liu, Genggeng Department of Information Engineering Fujian Business University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q-learning, the proposed global router adopts the Double Deep Q-Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router. © 2023 Saijuan Xu et al.

关键词： Reinforcement learning

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