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Mobile Edge computing for Multi-Services Digital Twin-Enabled IoT Heterogeneous Networks

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1845-1853页

作者： Liu, Weiqi Hossain, Mohammad Arif Ansari, Nirwan Auburn University at Montgomery Department of Computer Science and Computer Information Systems MontgomeryAL36117 United States Middle Tennessee State University Department of Engineering Technology MurfreesboroTN37132 United States New Jersey Institute of Technology Advanced Networking Laboratory Department of Electrical and Computing Engineering NewarkNJ07102 United States

A scheme for edge computing-enabled offloading in a digital twin (DT) enabled heterogeneous network (HetNet) of multi-services IoT devices (IDs) is proposed. This scheme optimizes the association and handover of IDs, offloading ratio, and resource allocation considering the number of IDs, deadline requirements, and resource capacities. The objective is to enhance future generation networks by considering the ID movement, diverse ID requests, and network heterogeneity. We formulate the problem as Joint ID assOciatIon, offloadiNg ratio, Wireless bandwidth and computing reSource allocation, and digital twin placEment (JOINWISE), aiming to minimize the task completion time of all IDs while considering ID movement. Since JOINWISE is a mixed-integer nonlinear problem, we decompose it into two sub-problems: the ID Association (IDA) problem and the offloading Ratio, DT plAcement, bandwiDth and computing resource allOcation (RADIO) problem. IDA can be solved by mapping it to a multi-dimensional multiple knapsacks problem. Due to the non-convexity, high dimension of decision variables, and dynamic HetNet environment of RADIO, we propose a deep deterministic policy gradient (DDPG) based reinforcement learning method to iteratively solve the two sub-problems. Simulation results have confirmed the effectiveness of our proposed scheme in tackling the JOINWISE problem. © 2015 IEEE.

关键词： Mobile edge computing

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IoT Device Authentication via RAM Trace Analysis: A Representation Learning Framework

IoT Device Authentication via RAM Trace Analysis: A Represen...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Iqbal, Asif Aman, Muhammad Naveed Sikdar, Biplab National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Nebraska-Lincoln School of Computing Nebraska United States

ISBN: (纸本)9798350351255

Recent advances in IoT, machine learning, and edge computing have driven transformative paradigms like smart cities, grids, healthcare, and transportation systems, providing efficient solutions. This has led to a pervasive proliferation of connected devices, ranging from high-power computers to low-power sensors. Yet, the complex IoT architecture poses numerous vulnerabilities, demanding robust security measures. Existing firmware attestation techniques often encounter obstacles due to proprietary constraints, necessitating access to the device's authentic firmware. To address this challenge, this paper proposes a novel software-based attestation framework that utilizes RAM traces from IoT devices for remote verification. By employing deep learning models trained in a representation learning paradigm, our framework empowers the remote verifier to authenticate the internal state of IoT devices. Leveraging data collected from real-world prototype devices, our approach achieves an impressive 100% detection rate for critical attacks on IoT devices with a false positive rate of 10-3. Remarkably, our framework preserves device availability and maintains low authentication latency, highlighting its efficacy and practicality for securing IoT ecosystems. © 2024 IEEE.

关键词： Firmware

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Analyzing the Impact of Context Representation and Scope in Code Infilling 24

Analyzing the Impact of Context Representation and Scope in ...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Heo, Jinseok Lee, Eunseok Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400705021

Existing studies solve software engineering tasks using code infilling through LLMC. They utilize context information, which refers to data near the target code of infilling, as input prompts. Although prompts are essential for infilling the target code, current studies use them without analyzing the impact of the representation and scope of context on code infilling. In this study, we analyzed how context representation and scope affect the performance of code infilling. We used XLCost, which contains code, comments, and a function comment for various programming languages. The combination of code and a function comment for context representation yielded the best code infilling performance. Furthermore, we found that the context scope is proportional to performance. Our analysis results can be applied in various tasks that involve code infilling in the future. © 2024 IEEE computer Society. All rights reserved.

关键词： Software engineering

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Joint Communication and Computation Resource Allocation for Emerging mmWave Multi-User 3D Video Streaming Systems

Joint Communication and Computation Resource Allocation for ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Badnava, Babak Chakareski, Jacob Hashemi, Morteza University of Kansas Department of Electrical Engineering and Computer Science United States New Jersey Institute of Technology College of Computing United States

ISBN: (纸本)9798350351255

We consider a multi-user joint rate adaptation and computation distribution problem in a millimeter wave (mmWave) virtual reality (VR) system. The VR system that we consider comprises an edge computing unit (ECU) that serves 360° videos to VR users. We formulate a multi-user quality of experience (QoE) maximization problem, in which VR users are assisted with the ECU to decode/render 360° videos. The ECU provides additional computational resources that can be used for processing video frames, at the expense of increased data volume and required bandwidth. To balance this trade-off, we leverage deep reinforcement learning (DRL) for joint rate adaptation and computational resource allocation optimization. Our proposed method, dubbed Deep VR, does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train Deep VR with real-world mmWave network traces and 360° video datasets to obtain evaluation results in terms of the average QoE, peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the Deep VR improves the users' QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 3.08 dB to 4.49 dB improvement in video quality in terms of PSNR, a 12.5x to 14x reduction in rebuffering time, and a 3.07 dB to 3.96 dB improvement in quality variation. © 2024 IEEE.

关键词： Video streaming

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Detection of Malicious URLs using Ensemble learning techniques

Detection of Malicious URLs using Ensemble learning techniqu...

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2023 IEEE Technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Arjun, D Shashank Samhitha, D Sai Padmavathi, A. Hemprasanna, A. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Electrical and Computer Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350384659

The growing risk of cyber-attacks and information vulnerability has become a major problem in today's dynamic digital environment. The necessity for strong security solutions is more critical than ever due to the development of attack techniques and the spread of social engineering strategies. Hackers commonly utilize malicious Uniform Resource Locators (URLs) to trick users and launch different kinds of attacks. This research uses the power of ensemble learning to tackle this problem by mixing various machine learning techniques like XGBoost, Extra Tree Classifier, and Random Forest. This ensemble model not only performs exceptionally well at identifying hazardous URLs, but it also provides a scalable cybersecurity solution, acting as a protector against developing online threats. © 2023 IEEE.

关键词： Machine learning

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Distortion minimization for multimedia transmission in NOMA HAP-UAV integrated aerial access networks

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Chinese Journal of Aeronautics 2022年第9期35卷 81-94页

作者： Guobin ZHANG Zhu HAN Hang XIN Xin WEI Boya DI School of Electrical Engineering and Intelligentization Dongguan University of TechnologyDongguan 523808China Department of Electrical and Computer Engineering and Department of Computer Science University of HoustonTX77004USA School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Department of Computing Imperial CollegeLondon SW72AzUK

Aerial access networks have been envisioned as a promising 6G solution to enhance the ground communication systems in both coverage and capacity. To better utilize the spectrum and fully explore different channel characteristics, this paper constructs an integrated network comprising the High Altitude Platform(HAP) and Unmanned Air Vehicles(UAVs) with the NonOrthogonal Multiple Access(NOMA) technology. In order to improve the transmission quality of images and videos, a power management scheme is proposed to minimize the distortion of the transmissions from the HAP and UAVs to the terminals. The power control is formulated as a non-convex problem constrained by the maximal transmit power and the minimal terminal rate requirements. The variable substitution and the first-order Tailor’s expansion is used to transform it into a sequence of convex problems, which are subsequently solved through the gradient projection method. Simulation demonstrates the signal distortion and error rate improvement achieved by the proposed algorithm.

关键词： Aerial Access Networks High Altitude Platform Non-Orthogonal Multiple Access Sixth-Generation Unmanned Air Vehicles

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Bridging the Gap Between Central and Local Decision-Making: The Efficacy of Collaborative Equilibria in Altruistic Congestion Games 63

Bridging the Gap Between Central and Local Decision-Making: ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ferguson, Bryce L. Paccagnan, Dario Pradelski, Bary S. R. Marden, Jason R. University of California Department of Electrical and Computer Engineering Santa BarbaraCA United States Imperial College Department of Computing London United Kingdom France

ISBN: (纸本)9798350316339

Congestion games are popular models often used to study the system-level inefficiencies caused by selfish agents, typically measured by the price of anarchy. One may expect that aligning the agents' preferences with the system-level objective-altruistic behavior-would improve efficiency, but recent works have shown that altruism can lead to more significant inefficiency than selfishness in congestion games. In this work, we study to what extent the localness of decision-making causes inefficiency by considering collaborative decision-making paradigms that exist between centralized and distributed in altruistic congestion games. In altruistic congestion games with convex latency functions, the system cost is a super-modular function over the player's joint actions, and the Nash equilibria of the game are local optima in the neighborhood of unilateral deviations. When agents can collaborate, we can exploit the common-interest structure to consider equilibria with stronger local optimality guarantees in the system objective, e.g., if groups of k agents can collaboratively minimize the system cost, the system equilibria are the local optima over k-lateral deviations. Our main contributions are in constructing tractable linear programs that provide bounds on the price of anarchy of collaborative equilibria in altruistic congestion games. Our findings bridge the gap between the known efficiency guarantees of centralized and distributed decision-making paradigms while also providing insights into the benefit of inter-agent collaboration in multi-agent systems. © 2024 IEEE.

关键词： Multi agent systems

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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A Procedure for Achieving Network-on-Demand for Intent-Driven Purposes in 5G Era Using Network-Slicing Technique 2

A Procedure for Achieving Network-on-Demand for Intent-Drive...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Vishnu, Ram Ov Imoru, Odunayo Thomas, Sadiq Guda, Blessed Amadasun, Kennedy Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria India University of Johannesburg Department of Electrical and Computer Engineering South Africa Nile University Department of Computer Engineering Nigeria School of Computing and Engineering University of Sunderland Department of Electrical/Electronic Engineering United Kingdom

ISBN: (纸本)9798350358834

Network slicing is a key feature of 5G that allows for the creation and running of multiple virtual networks, known as 'slices,' on shared infrastructure. These slices can be customized for specific applications and requirements, and offer greater flexibility than previous network technologies. Additionally, the use of network slicing can generate new revenue opportunities through automation, leading to profitable growth at a large scale. This paper understudied the processes involved in achieving this and thereby proposed a sequence of procedures that will seamlessly realise this, the work first considered Slice Network based on the Business request of the industry vertical, then the aggregation of intent based on the request, thereafter assign slice based on the business request, next a check is carried out if the Network policies are met, the RAN-Slicing is employed, finally, it deploys Network Orchestration, which manages, monitor and grants End-to-End Access, this sequence will help network operators in reducing expenses (OPEX) and capital expenditure (CAPEX). The use of this technology will greatly increase operational efficiency and speed up the rolling out of 5G network services. It will also enable operators to offer a range of new services to customers using the same physical network infrastructure. © 2023 IEEE.

关键词： 5G mobile communication systems

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