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IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Changchuan Yin Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

In this paper, the problem of using several controlled unmanned aerial vehicles (UAVs) to localize a target UAV in real time is investigated. In the considered model, the controlled UAV consists of one active UAV and four passive UAVs. Each passive UAV receives signals transmitted from the active UAV and reflected by the target UAV, and then estimates the distance from the active UAV to the target UAV and then from the target UAV to the passive UAV. Each passive UAV then transmits this distance information to a base station (BS), which estimates the location of the target UAV. Since the target UAV will change its location according to its performed task, each controlled UAV must optimize its trajectory to continuously localize the target UAV. This trajectory design problem is formulated as an optimization problem whose goal is to jointly optimize the trajectories of active and passive UAVs so as to maximize the target UAV positioning accuracy. To solve this problem, a Z function decomposition based reinforcement learning (ZD-RL) method is proposed. Compared to value function decomposition based RL (VD-RL), the proposed method can find the probability distribution of the sum of future rewards to accurately estimate the expected value of the sum of future rewards, thus finding better trajectories for controlled UAVs and improving target UAV positioning accuracy. Simulation results show that the proposed ZD-RL method can reduce the positioning errors by up to 58.3% and 84.8%, compared to VD-RL and independent DRL methods, respectively.

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Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach

arXiv

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arXiv 2025年

作者： Li, Jichen Xie, Lijia Huang, Hanting Zhou, Bo Song, Binfeng Zeng, Wanying Deng, Xiaotie Zhang, Xiao Zhongguancun Laboratory China Center on Frontiers of Computing Studies Computer Science Department Peking University China LMIB and School of Mathematical Sciences Beihang University China LMIB and Institute of Artificial Intelligence Beihang University China Hangzhou International Innovation Institute of Beihang University China

Strategic mining attacks, such as selfish mining, exploit blockchain consensus protocols by deviating from honest behavior to maximize rewards. Markov Decision Process (MDP) analysis faces scalability challenges in modern digital economics, including blockchain. To address these limitations, reinforcement learning (RL) provides a scalable alternative, enabling adaptive strategy optimization in complex dynamic environments. In this survey, we examine RL’s role in strategic mining analysis, comparing it to MDP-based approaches. We begin by reviewing foundational MDP models and their limitations, before exploring RL frameworks that can learn near-optimal strategies across various protocols. Building on this analysis, we compare RL techniques and their effectiveness in deriving security thresholds, such as the minimum attacker power required for profitable attacks. Expanding the discussion further, we classify consensus protocols and propose open challenges, such as multi-agent dynamics and real-world validation. This survey highlights the potential of reinforcement learning (RL) to address the challenges of selfish mining, including protocol design, threat detection, and security analysis, while offering a strategic roadmap for researchers in decentralized systems and AI-driven analytics. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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Relativistic Digital Twin: Bringing the IoT to the Future

arXiv

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arXiv 2023年

作者： Sciullo, Luca De Marchi, Alberto Trotta, Angelo Montori, Federico Bononi, Luciano Felice, Marco Di Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems University of Bologna Italy Institute of Applied Mathematics and Scientific Computing Department of Aerospace Engineering University of the Bundeswehr Munich Germany

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario. Experiments show that the generated DT can estimate the behavior of its real counterpart after an observation stage, regardless of the considered scenario. © 2023, CC BY.

关键词： Internet of things

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Enabling Support for TLS and TLS Fingerprinting in Network Stack Tester

Enabling Support for TLS and TLS Fingerprinting in Network S...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Nitin Dabi Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Transport Layer Security (TLS) is the standard protocol widely used for securing client-server communications over the Internet. It ensures the confidentiality and integrity of data transmitted between systems. With the rising deployment of TLS 1.3, which is the latest version of the protocol, stronger encryption and improved security features are introduced. The adoption of TLS 1.3 brings about the need for techniques such as TLS fingerprinting. TLS fingerprinting involves analyzing the unique characteristics of TLS handshakes to identify and classify different implementations or configurations of TLS. However, researchers currently lack a dedicated platform to test TLS and TLS fingerprinting techniques in a controlled environment. To address this gap, we aim to enhance NeST (Network Stack Tester) by adding support for TLS and TLS fingerprinting. NeST is a Python tool for network emulation that internally uses Linux network namespaces and Linux utilities to create virtual testbeds, perform experiments using various network protocols and collect statistics. By integrating TLS and TLS fingerprinting into NeST, we provide an easy-to-use tool that enables researchers to experiment with TLS, analyze TLS traffic, and evaluate the effectiveness of TLS fingerprinting techniques.

关键词： Protocols Linux Emulation Fingerprint recognition Communications technology Internet Encryption

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Design of Subsea storage tanks for Arctic conditions - heat treatment of materials

引用

Materials Today: Proceedings 2023年

作者： Kishor Tirumanur Shanmugavelu, Arvind Muraliraja, R. Shanmugam, Ragavanantham Pal Singh Pawar, Mayur Vishwakarma, Rajoo Sarkar, Proshanta Department of Naval Architecture and Offshore Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 Tamil Nadu India Advanced Manufacturing Engineering Technology Navajo Technical University NM 87313 United States Department of Civil Engineering IES College of Technology Bhopal 462044 Madhya Pradesh India Department of Mechanical Engineering Suyash Institute of Information Technology Gorakhpur 273016 Uttar Pradesh India Department of Computer Science Kalinga University Naya Raipur 492101 Chhattisgarh India

The Arctic region contains a plethora of recoverable hydrocarbon wealth in the form of oil and gas in existing conventional wells and other undiscovered fields. The main challenges faced in the Arctic region are both environmental and operational. Some environmental challenges are low ambient temperature, winterization, severity and unpredictability of the weather. However, the issue of multi-phase flow in the current operation coupled with the decline of the reservoir pressure are the major concerns in this gas field. In the early stages of field life, the reservoir pressure is sufficient to allow the natural flow from reservoir formation to surface without additional supporting compression. Unfortunately, the pressure naturally declines throughout the entire life cycle of the field. To achieve a better production profile in the arctic region the idea is to implement a Subsea processing concept employing a Subsea Storage Tank (SST) in order to maximize production from additional infield wells, introducing a novel step in this field development phase that will change the entire oil and gas industry especially for subsea processing field architecture. The incorporation of SST in the subsea system will enhance the production rate as well as it avoids the transportation problems through pipe lines. By this work, the production rate is increased by 50% and decrease the oil spill accidents considerably. © 2023 Elsevier Ltd. All rights reserved.

关键词： Heat transfer

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Gastrointestinal disease prediction using transfer learning

Gastrointestinal disease prediction using transfer learning

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International Conference on computer Vision and Internet of Things 2023 (ICCVIoT'23)

作者： R. Sujatha D. Helen U. Hemamalini V. Divya A. Priya Dharshini School of Information Technology & Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology VELS Institute of Science technology and Advanced Studies Pallavaram Chennai India

According to the Global Cancer Observatory's 2018 estimations, gastrointestinal cancer is the third most common cause of cancer deaths and the fifth most common cause of being diagnosed with cancer worldwide. Diagnosis is crucial, and gastroscopy is used to detect stomach cancer early to enhance patient survival. This study proposed a Deep Learning based computer-Aided Diagnostics (CADx) method to identify gastroscopy disease. The dataset is trained with an adversarial training technique. The proposed approach uses the deep convolution neural network based on VGG16, VGG19, and InceptionV3. The dataset is trained with an adversarial training technique. The Inception V3 has the best accuracy among them, with an accuracy in the training of 94.44% and a validation accuracy of 84.75%, with a minor loss compared to VGG16 and VGG19 models.

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Design and Development of an Emulation Model for VPN and VPN Bonding

Design and Development of an Emulation Model for VPN and VPN...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Kaustubh Sharma Mohit P. Tahiliani Vishal J. Rathod Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore Karnataka India Internet of Things - Research and Development Group Centre for Development of Advanced Computing (C-DAC) Bengaluru Karnataka India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Virtual Private Networks (VPNs) have become indispensable for organizations seeking secure remote network access, with a significant rise in their adoption. While the COVID-19 pandemic initially fueled the surge in remote work and VPN usage, the trend has continued post-pandemic as organizations increasingly opt for hybrid work models. VPNs allow users to establish secure connections to their organization’s network from any location, ensuring the confidentiality of transmitted data. Moreover, VPN bonding, which combines multiple VPN connections into a unified interface, improves performance and reliability, particularly in areas with limited Internet connectivity. As the usage of VPN and VPN bonding technologies continues to expand, a growing demand arises for research in this field, and consequently, the need for robust emulators and simulators. However, existing network emulators or simulators currently lack comprehensive support for VPN-related technologies. To address these limitations, this paper aims to develop intuitive and user-friendly Application Programming Interfaces (APIs) for emulating VPN and VPN bonding in Network Stack Tester (NeST), a powerful Python package designed to facilitate network emulation for experienced researchers and individuals new to the field. This work utilizes OpenVPN, a secure tunneling daemon, to integrate the support of VPN emulation in NeST. By augmenting the capabilities of NeST, this work intends to provide researchers with a valuable toolset for exploring and testing VPN and VPN bonding technologies across diverse network environments.

关键词： Emulation Organizations Tunneling Remote working Virtual private networks Reliability Bonding

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Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor

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SN Applied sciences 2023年第7期5卷 1-9页

作者： Jawarneh, Malik Arias-Gonzáles, José Luis Gandhmal, Dattatray P. Malik, Rami Qays Rane, Kantilal Pitambar Omarov, Batyrkhan Mahapatra, Cosmena Shabaz, Mohammad Faculty of Computing Sciences Gulf College Muscat Oman University of British Columbia Arequipa Peru Department of Computer Science and Engineering N. B N. B. Navale Sinhgad College of Engineering Solapur India Medical Instrumentation Techniques Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Electronics and Telecommunications Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Al-Farabi Kazakh National University Almaty Kazakhstan Vivekananda Institute of Professional Studies New Delhi India Arba Minch University Arba Minch Ethiopia

Malignant growth in liver results in liver tumor. The most common types of liver cancer are primary liver disease and secondary liver disease. Most malignant growths are benign tumors, and the condition they cause, essential liver disease, is the end result. Cancer of the liver is a potentially fatal disease that can only be cured by combining a number of different treatments. Machine learning, feature selection and image processing have the capability to provide a framework for the accurate detection of liver diseases. The processing of images is one of the components that come together to form this group. When utilized for the purpose of reviewing previously recorded visual information, the instrument performs at its highest level of effectiveness. The importance of feature selection on machine learning algorithms for the early and accurate diagnosis of liver tumors is discussed in this article. The input consists of images from a CT scan of the liver. These images are preprocessed by discrete wavelet transform. Discrete wavelet transforms increase resolution by compressing the images. Images are segmented in parts to identify region of interest by K Means algorithm. Features are selected by grey wolf optimization technique. Classification is performed by Gradient boosting, support vector machine and random forest. GWO Gradient boosting is performing better in accurate classification and prediction of liver cancer. © 2023, The Author(s).

关键词： Discrete wavelet transforms

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Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

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IEEE Transactions on Signal Processing 2025年

作者： Wang, Zhe Zhang, Jiayi Bjornson, Emil Niyato, Dusit Ai, Bo Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China KTH Royal Institute of Technology Department of Computer Science Stockholm114 28 Sweden Nanyang Technological University College of Computing & Data Science 639798 Singapore

In this paper, we explore the low-complexity optimal bilinear equalizer (OBE) combining scheme design for cell-free massive multiple-input multiple-output networks with spatially correlated Rician fading *** provide a spectral efficiency (SE) performance analysis framework for both the centralized and distributed processing schemes with bilinear equalizer (BE)-structure combining schemes applied. The BE-structured combining is a set of schemes that are constructed by the multiplications of channel statistics-based BE matrices and instantaneous channel estimates. Notably, we derive closed-form achievable SE expressions for centralized and distributed BE-structured combining schemes. We propose one centralized and two distributed OBE schemes: Centralized OBE (C-OBE), Distributed OBE based on Global channel statistics (DG-OBE), and Distributed OBE based on Local channel statistics (DL-OBE), which maximize their respective SE expressions. OBE matrices in these schemes are tailored based on varying levels of channel statistics. Notably, we obtain new and insightful closed-form results for the C-OBE, DG-OBE, and DL-OBE combining schemes. Numerical results demonstrate that the proposed OBE schemes can achieve excellent SE, even in scenarios with severe pilot contamination. © 2025 IEEE. All rights reserved.

关键词： Fading channels

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On the Impact of Predictor Serial Correlation on the LASSO

arXiv

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arXiv 2024年

作者： Tonini, Simone Chiaromonte, Francesca Giovannelli, Alessandro Institute of Economics & L’EMbeDS Sant’Anna School of Advanced Studies Pisa Italy Department of Statistics Penn State University United States Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’Aquila Italy

We explore inference within sparse linear models, focusing on scenarios where both predictors and errors carry serial correlations. We establish a clear link between predictor serial correlation and the finite sample performance of the LASSO, showing that even orthogonal or weakly correlated stationary AR processes can lead to significant spurious correlations due to their serial correlations. To address this challenge, we propose a novel approach named ARMAr-LASSO (ARMA residuals LASSO), which applies the LASSO to predictor time series that have been pre-whitened with ARMA filters and lags of dependent variable. Utilizing the near-epoch dependence framework, we derive both asymptotic results and oracle inequalities for the ARMAr-LASSO, and demonstrate that it effectively reduces estimation errors while also providing an effective forecasting and feature selection strategy. Our findings are supported by extensive simulations and an application to real-world macroeconomic data, which highlight the superior performance of the ARMAr-LASSO for handling sparse linear models in the context of time series. © 2024, CC BY.

关键词： Time series

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