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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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Transaction transmission model for blockchain channels based on non-cooperative games

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Science China(Information Sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecur...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Phan, Quoc Bao Nguyen, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350324136

This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information. © 2024 IEEE.

关键词： GANs Images denoising Kyber number theoretic transform

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Options for Application of Distributed Ledger Technologies in Development and Operation of Energy Communities 7

Options for Application of Distributed Ledger Technologies i...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Svetec, Erica Herencic, Lin Hrga, Alen Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia

ISBN: (纸本)9789532901160

Deep integration of variable renewable energy sources in electrical power systems requires widespread use of digital technologies and novel citizen-oriented business models. The emergence of distributed ledger technologies (DLTs) has spurred the development of pilot projects and the assessment of the opportunities for the application of DLTs in the energy sector. In this paper, we examine DLTs considering their key features and applications in the energy sector, with a focus on their potential use in energy communities. As a case study, we present the pilot site in the city of KriÅ½evci in Croatia, where an energy community is being established. Based on this case, we assess the potential use cases, advantages, and possible drawbacks in the application of DLTs in comparison with the business-as-usual non-DLT solutions. © 2022 University of Split, FESB.

关键词： Blockchain

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Enhancing Perception for Autonomous Driving systems with Dual-Vision Input Cycle GAN for Night-To-Day Image Translation 8

Enhancing Perception for Autonomous Driving Systems with Dua...

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8th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Fernando, K.J.P. Lakmal, H.K.I.S. Dissanayake, M.B. Kalansooriya, L.P. NSBM Green University Department of Electrical Electronic & Systems Engineering Homagama Sri Lanka NSBM Green University Department of Mechatronic & Industrial Engineering Homagama Sri Lanka University of Peradeniya Department of Electrical and Electronic Engineering Peradeniya Sri Lanka General Sir John Kotelawala Defence University Faculty of Computing Ratmalana Sri Lanka

ISBN: (纸本)9798331509170

Autonomous Driving systems (ADS) and Advanced Driver Assistance systems (ADAS) heavily rely on vision-based data for critical decision-making. However, these systems face significant performance limitations in low-light conditions where conventional RGB vision inputs struggle to capture information due to low light. Current solutions, including high-gain cameras or LiDAR-based approaches, are expensive and increase system complexity. This study presents a novel approach to address these challenges by employing a night-To-day image translation model with modified CycleGAN. The proposed model translates RGB and infrared (IR) inputs into a daytime representation, enhancing overall perception and object detection in low-light scenarios. Unlike existing methods, the model operates in an unsupervised learning framework, avoiding the need for large, paired datasets, which are often difficult to obtain. The study also introduces architectural modifications to the CycleGAN, including a dualinput framework and refined architecture, to optimize performance across varied conditions. The model's performance is demonstrated through reference-less evaluation metrics, achieving average scores of FID 8.04, NIQE 4.92, and BRISQUE 36.3. These results illustrate the potential of the model to enhance the safety and operational stability of autonomous driving systems in low-light environments. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Big Data Analytics for Business Sustainability: Review Study 4

Big Data Analytics for Business Sustainability: Review Study

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4th International Conference on Distributed Sensing and Intelligent systems, ICDSIS 2023

作者： Akour, Iman Elhoseny, Mohamed Al-Amawi, Amaal Akila, Ayman Department of Information Systems College of Computing & Informatics University of Sharjah United Arab Emirates Department of Management Information Systems Isra University Jordan Department of Electrical Engineering University of Sharjah United Arab Emirates

ISBN: (纸本)9781839539954

Recently, many businesses and industries involved sustainability in their strategies. The demand for sustainable development in business has drawn the attention of the government and academia and emerged as a broadly well-known trend. The aim of this project is to develop an integrated approach of the sustainable business from a theoretical perspective integrated with the current wave of big data analytics. To achieve this, aim an advance methodology is employed for the assessment of business sustainability based on the proposed big data analytics model from consumer perception dimension. In addition, this research will quantify the effect of big data analytics on business sustainability. It also proposes a scale for measuring and monitoring sustainability in the industry benchmarking the experiences of big data analytics usage globally and locally. The method used to achieve the mentioned aim is a qualitative approach. Articles published from 2015 to 2022 will be reviewed to get the findings and results of this review. This study contributes to the advancement of knowledge and literature by offering a comprehensive scale based on big data analytics to measure sustainability in the industries. Moreover, this research project is expected to produce research findings that will enhance our understanding of the importance of data analytics in business sustainability. Many literatures showed the impact of data analytics on business performances, but there are less articles that demonstrated the impact of big data analytics on business sustainability which aimed to be explored by this research project. © The Institution of engineering & Technology 2023.

关键词： Sustainable development goals

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Automated 3D Urban Landscapes Visualization Using Open Data Sources on the Example of the City of Zagreb

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KN - Journal of Cartography and Geographic Information 2022年第2期72卷 139-152页

作者： Komadina, Adrian Mihajlović, Željka Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb 10000 Croatia

Geographical location visualization is important to create virtual environments when performing simulations. This study uses publicly available data sources to create three-dimensional (3D) views of geographical locations, focusing on the city of Zagreb as a case study, and presents an automated solution that integrates data from four different sources to achieve this. Publicly available data sources (OpenStreetMap, Google Maps Elevation API, MapBox Static Images API) were used, and GIS Zrinjevac which is unique to Zagreb. Based on these sources, individual 3D objects were generated and displayed virtually in the form of an interactive 3D map. The proposed solution attempts to balance the differing levels of detail achieved using other techniques. The solution was implemented in the form of an application created using the Unity game engine. The results were analyzed to evaluate the solution’s validity and the created application’s performance. The analysis showed that the total execution time dependence is quadratically polynomial to the amount of retrieved data, and linear to the number of height map points. Examining the application’s update rate showed that the buildings and individual models used for polygon and point data had the greatest impact. Finally, possible improvements, alternative approaches, and advantages and disadvantages of the proposed solution are compared with other techniques used in this research area. © 2022, Deutsche Gesellschaft für Kartographie e.V.

关键词： Cartographic data Data visualization GIS Zrinjevac Google maps elevation API OpenStreetMap Virtual environment

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Illusion Spaces in VR: The Interplay between Size and Taper Angle Perception in Grasping 25

Illusion Spaces in VR: The Interplay between Size and Taper ...

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Zhang, Jian Johal, Wafa Knibbe, Jarrod School of Computing and Information Systems University of Melbourne MelbourneVIC Australia School of Electrical Engineering and Computer Science The University of Queensland St LuciaQLD Australia

ISBN: (纸本)9798400713941

Leveraging the integration of visual and proprioceptive cues, research has uncovered various perception thresholds in VR that can be exploited to support haptic feedback for grasping. While previous studies have explored individual dimensions, such as size, the combined effect of multiple geometric properties on perceptual illusions remains poorly understood. We present a two-alternative forced choice study investigating the perceptual interplay between object size and taper angle. We introduce an illusion space model, providing detailed insights into how physical and virtual object configurations affect human perception. Our insights reveal how, for example, as virtual sizes increase, users perceive that taper angles increase, and as virtual angles decrease, users overestimate sizes. We provide a mathematical model of the illusion space, and an associated tool, which can be used as a guide for the design of future VR haptic devices and for proxy object selections. © 2025 Copyright held by the owner/author(s).

关键词： Virtual reality

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Performance Evaluation of Intrusion Detection systems on the TON_IoT Datasets Using a Feature Selection Method 24

Performance Evaluation of Intrusion Detection Systems on the...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Maseno, Elijah Mwandata Wang, Zenghui Sun, Yanxia Department of Computing and Information Technology Mama Ngina University College Gatundu Nairobi Kenya Department of Electrical and Smart Systems Engineering Centre for Augmented Intelligence and Data Science University of South Africa Florida Gauteng South Africa Department of Electrical and Electronic Engineering Science University of Johannesburg Gauteng Johannesburg South Africa

ISBN: (纸本)9798400718182

As Internet of Things (IoT) technology develops so quickly, security issues with IoT devices have come to light. IoT is an array of intelligent devices connected via a network to provide various services. The amount of data generated by these devices has an impact on how well the current intrusion detection systems (IDS) function. The generated dataset consists of irrelevant features which reduces the performance of IDS, making IoT ecosystem vulnerable to cyberattacks. The researchers have suggested the feature reduction technique as a potential solution to the current problem. The proposed method seeks to reduce the feature count by removing the redundant feature subset. Several machine learning methods have been successfully implemented in this discipline. This study proposed the application of hybrid feature reduction technique. The research combined Convolutional neural network (CNN) and Long short-term memory (LSTM);CNN extracted local features and decreased dimensionality, while LSTM identified long-term relationships in the data. SVM and Random Forest classifiers models were used to classify the chosen feature subset. This study employed the TON_IoT Datasets, an up-to-date dataset, to evaluate the model. During data preprocessing, the study applied SMOTETomek data pre-processing technique to address class imbalance in the dataset. With the decreased feature subset, the classification models fared reasonably well;RF had a 98% accuracy rate while SVM had a 91% accuracy rate, showcasing the suggested methodology’s potential for creating efficient IDS. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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