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Careful Resume: Design and Analysis with Picoquic over Satellite Paths 12

Careful Resume: Design and Analysis with Picoquic over Satel...

12th Advanced Satellite Multimedia Systems Conference and the 18th Signal Processing for Space Communications Workshop, ASMS/SPSC 2025

作者： Hofstatter, Matthias Deutschmann, Jörg Secchi, Raffaello Fairhurst, Gorry German, Reinhard Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Networks and Communication Systems Germany University of Aberdeen Department of Engineering United Kingdom

ISBN: (纸本)9798331522353

Slow Start increases the congestion window by doubling each round trip until congestion is detected. As a result, reaching optimal speed typically takes several round trip times, especially important in high bandwidth-delay product networks. Geostationary satellite communications now use Performance Enhancing Proxies to accelerate the growth of the congestion window. However, these are ineffective for connections with encrypted headers or UDP traffic. We propose a new method, Careful Resume, that enables a rapid ramp-up of congestion control by reusing previous parameters. We implement Careful Resume in Picoquic and run performance tests using an emulated geostationary satellite path, three real geostationary satellite networks, the low Earth orbit satellite megaconstellation Starlink, and a cellular 5G network. Results confirm that Careful Resume accelerates the ramp-up process, reducing total transmission time. Even for connections with lower bandwidth-delay products, the network congestion point can be reached much more quickly. Benefits are particularly pronounced for paths with high bandwidth-delay products and medium-sized transfers such as web traffic. © 2025 IEEE.

关键词： Geostationary satellites

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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FLSN-MVO: Edge Computing and Privacy Protection Based on Federated Learning Siamese Network With Multi-Verse Optimization Algorithm for Industry 5.0

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3443-3458页

作者： Yin, Shoulin Li, Hang Laghari, Asif Ali Teng, Lin Reddy Gadekallu, Thippa Almadhor, Ahmad Shenyang Normal University Software College Shenyang110034 China Harbin Engineering University School of Information and Communication Engineering Harbin150000 China Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India Al Jouf University College of Computer and Information Sciences Department of Computer Engineering and Networks Sakaka72388 Saudi Arabia

With the development of deep learning technology, artificial intelligence has important applications in all aspects of society, but the lack of data has become a vital factor restricting the further evolution of artificial intelligence in industry 5.0. Federated learning can effectively use the edge node data and solve the data problem of artificial intelligence model training by sharing gradient. In federated learning, the terminal transmits the updated model parameter values instead of the primordial data to the server, thus becoming a key technology to ensure data privacy and security in edge computing. However, since attackers can use the shared gradient to launch malicious attacks to steal users’ privacy, how to securely upload the gradient and aggregate it has become an important topic to ensure privacy security in federated learning. Therefore, this paper proposes an edge computing and privacy protection based on federated learning Siamese network with multi-verse optimization algorithm for industry 5.0. This scheme can reduce the expenditure of endpoint participation in federated learning while protecting user privacy. In the federated learning structure, the feature information of the input samples is mapped to a new output vector through the subnetwork of the Siamese network, and the approximation degree between the input samples is judged by comparing the similarity degree between the output vectors. The parameters of the network are optimized by multi-verse optimization algorithm to reduce the convergence time. Meanwhile, an adaptive weight aggregation algorithm is designed to reduce the degradation of model performance and stability caused by data quality differences, so as to improve the accuracy of the model and accelerate the model to reach the optimal value. Finally, comprehensive experiments on three public standard data sets show that the proposed method achieves higher model accuracy and faster model convergence than the most advanced methods. ©

关键词： Federated learning

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Proactive Robust Hardening of Resilient Power Distribution Network: Decision-Dependent Uncertainty Modeling and Fast Solution Strategy

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14953-14966页

作者： Ma, Donglai Cao, Xiaoyu Zeng, Bo Jia, Qing-Shan Chen, Chen Zhai, Qiaozhu Guan, Xiaohong Xi’an Jiaotong University School of Automation Science and Engineering Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi’an710049 China University of Pittsburgh Department of Industrial Engineering Department of Electrical and Computer Engineering PittsburghPA15106 United States Tsinghua University Center for Intelligent and Networked Systems Department of Automation Beijing100084 China Xi’an Jiaotong University School of Electrical Engineering Shaanxi Xi’an710049 China

To address the power system hardening problem, traditional approaches often adopt robust optimization (RO) that considers a fixed set of concerned contingencies, regardless of the fact that hardening some components actually renders relevant contingencies impractical. In this paper, we directly adopt a dynamic uncertainty set that explicitly incorporates the impact of hardening decisions on the worst-case contingencies, which leads to a decision-dependent uncertainty (DDU) set. Then, a DDU-based robust-stochastic optimization (DDU-RSO) model is proposed to support the hardening decisions on distribution lines and distributed generators (DGs). Also, the randomness of load variations and available storage levels is considered through stochastic programming (SP) in the innermost level problem. Various corrective measures (e.g., the joint scheduling of DGs and energy storage) are included, coupling with a finite support of stochastic scenarios, for resilience enhancement. To relieve the computation burden of this new hardening formulation, an enhanced customization of parametric column-and-constraint generation (P-C&CG) algorithm is developed. By leveraging the network structural information, the enhancement strategies based on resilience importance indices are designed to improve the convergence performance. Numerical results on 33-bus and 118-bus test distribution networks have demonstrated the effectiveness of DDU-RSO aided hardening scheme. Furthermore, in comparison to existing solution methods, the enhanced P-C&CG has achieved a superior performance by reducing the solution time by a few orders of magnitude. © 2004-2012 IEEE.

关键词： Stochastic programming

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A Robust Speech Encryption Method Using Segmentation and Chaotic Keys for Enhanced Audio Security 3rd

A Robust Speech Encryption Method Using Segmentation and Ch...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Abu-Faraj, Mua’ad Al-Hyari, Abeer Alqadi, Ziad Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Department of Electrical Engineering Al-Balqa Applied University As Salt19117 Jordan Department of Computers and Networks Engineering Al-Balqa Applied University Amman15008 Jordan

ISBN: (纸本)9783031876462

This paper presents an innovative method for safeguarding audio files. The proposed technique leverages an advanced primary key with a complex structure, offering a vast keyspace resistant to hacking. The private key includes details for segmenting the speech audio file and generating unique chaotic keys for encryption. These keys are extremely sensitive to minor changes in the primary key, flagging any slight alteration during decryption as a hacking attempt. The method is tested and implemented on various speech signals, with the results analyzed to highlight the main features and improvements. The findings demonstrate how the technique improves encryption and decryption by optimizing metrics such as MSE, PSNR, CC, and NSCR. Speed analysis verifies the method’s performance by maximizing the throughput of the speech cryptography process. The results are compared with existing approaches to show the superiority and enhancements provided. This method is straightforward to implement, as it does not require modifying the algorithm when altering the speech audio file or the private key, making it a versatile solution for various speech encryption needs. This research is highly relevant to network security, as it ensures the integrity and confidentiality of transmitted speech signals, preventing unauthorized access and improving overall communication security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Speech enhancement

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Enhancing Security and Privacy in Federated Learning Using Low-Dimensional Update Representation and Proximity-Based Defense

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3372-3385页

作者： Li, Wenjie Fan, Kai Zhang, Jingyuan Li, Hui Lim, Wei Yang Bryan Yang, Qiang Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named Federated Learning with Low-Dimensional Update Representation and Proximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs $\mathsf {LinfSample}$LinfSample method, enabling clients to compute the $l{ }$l∞ norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security. © 1989-2012 IEEE.

关键词： Federated learning

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Trustworthy Analytics in ETSI ZSM: A 5G Security Case Study

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 3292-3304页

作者： Radoglou-Grammatikis, Panagiotis Katsaros, Efklidis Syrmos, Evangelos Katsikas, Georgios P. Klonidis, Dimitrios Argyriou, Vasileios Lagkas, Thomas Sarigiannidis, Panagiotis K3Y Ltd. Research and Development Department Sofia1700 Bulgaria University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece UBITECH LIMITED Research and Development Department Limassol3020 Cyprus Kingston University London Department of Networks and Digital Media KT1 2EE United Kingdom Democritus University of Thrace Department of Computer Science Kavala65404 Greece

Towards the advent of the sixth generation (6G) wireless networks, smart technologies play a key role in the end-to-end automation of services across multiple domains. In particular, they create a new reality with multiple benefits, including intent-driven management, ultra-speed communication services and holistic integration within the Internet of Things (IoT). In this context, the Zero-touch Network and Service Management (ZSM) group within the European Telecommunications Standards Institute (ETSI) aims to provide an architectural framework which will allow the zero-touch orchestration of network services in a multi-domain fashion. Therefore, in this paper, we investigate the role of cross-domain storage, communication and analytics services within the architectural framework of ETSI ZSM. For this purpose, we take into account a particular case study which focuses on the orchestration of security services within the 5G core. More specifically, the deployment of a new User Plane Function (UPF) equipped with intrusion detection services that leverage Artificial Intelligence (AI) is investigated. For this purpose, the aforementioned cross-domain services are used to assess the security of the respective AI models before the onboarding of the new UPF within the 5G core. Based on this case study, a new security game is investigated, exploring and modelling the strategies of potential attackers and defenders. Furthermore, the architectural design and implementation of the cross-domain services are provided. Finally, the evaluation results show that the cross-domain analytics services are able to assess the security and resilience of the AI models and guide the orchestration functions. © 2020 IEEE.

关键词： 5G mobile communication systems

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Generative Adversarial Network Enhanced DRL for ISAC With Double Active RISs

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IEEE Internet of Things Journal 2025年第10期12卷 13487-13499页

作者： Zhang, Jifa Sheng, Min Xing, Chengwen Liu, Junyu Zhao, Nan Karagiannidis, George K. Dalian University of Technology School of Information and Communication Engineering Liaoning Dalian China Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Greece

Integrated sensing and communication (ISAC) is a promising paradigm to alleviate spectrum congestion and facilitate a variety of emerging internet of things (IoT) applications. However, the direct links from the ISAC base station (BS) to the users may be blocked due to the obstacles. In this paper, we investigate the double-active reconfigurable intelligent surfaces (RISs) assisted ISAC, where two active RISs are used to establish virtual line-of-sight (LoS) links from the ISAC BS to the users. In addition, the sum of the minimum sensing signal-to-interference-plus-noise ratios (SINRs) among multiple targets during a series of time slots is maximized, subject to quality of service (QoS) and transmit power constraints, through the joint optimization of transmit, reflection and receive beamforming. We first transform this non-convex optimization problem in the dynamic environment into a Markov decision process, and then propose a double delayed deep deterministic policy gradient (TD3)-based algorithm to solve it. Moreover, to enhance the generalization and stability, we integrate the generative adversarial network (GAN) into the TD3 algorithm and propose a GAN-TD3-based algorithm to handle the beamforming optimization problem. Compared with the TD3-based algorithm, the proposed GAN-TD3-based algorithm achieves the better performance and higher stability at the cost of higher computational complexity and slower convergence speed. Simulation results are presented to verify the effectiveness of our proposed algorithms and the superiority of the active RIS over the passive counterpart. © 2014 IEEE.

关键词： Generative adversarial networks

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadvand, Fatemeh Imani, Mahmood Hosseini Andani, Hamid Taheri Andani, Mansour Taheri Politecnico di Torino Department of Control and Computer Engineering Torino Italy Torino Italy Isfahan University of Technology Department of Electronic Engineering Isfahan Iran School of Manufacturing Systems and Networks Arizona State University TempeAZ United States

ISBN: (纸本)9798331541125

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price. © 2025 IEEE.

关键词： Long short-term memory

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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