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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

Energy-Efficient Data Offloading for Earth Observation Satellite Networks

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Energy-Efficient Data Offloading for Earth Observation Satel...

IEEE Conference on Vehicular Technology (VTC)

作者： Lijun He Ziye Jia Juncheng Wang Feng Wang Erick Lansard Chau Yuen School of Software Northwestern Polytechnical University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Department of Computer Science Hong Kong Baptist University China Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In Earth Observation Satellite Networks (EOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving the data offloading efficiency. As such, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in EOSNs, aiming to balance the objectives of reducing the total energy consumption and increasing the sum weights of tasks. First, we derive the optimal power allocation solution to the joint optimization problem when the task scheduling policy is given. Second, leveraging the conflict graph model, we transform the original joint optimization problem into a maximum weight independent set problem when the power allocation strategy is given. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the sum weights of tasks and the total energy consumption, thus achieving superior system performance over the current best alternatives.

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Mechanical Vibrations on a Deployable Nanosatellite Antenna: SAR Performance Analysis

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Mechanical Vibrations on a Deployable Nanosatellite Antenna:...

European Conference on Antennas and Propagation, EuCAP

作者： Andrew C. M. Austin Timothy Pelham Simone Mencarelli Annalisa Tresoldi Mohammed Dabboor Guglielmo Aglietti Michael J. Neve Communication Systems and Networks Group University of Bristol Bristol UK Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea—Auckland Space Institute University of Auckland Auckland New Zealand Science and Technology Branch Environment and Climate Change Canada Dorval QC Canada

The mechanical vibrations of a high-strain glass-fibre composite deployable X-band synthetic aperture radar (SAR) antenna structure are investigated using a finite element method. The antenna is approximately $\mathrm{2}\ \mathrm{m}\times \mathrm{0}.\mathrm{3}\ \mathrm{m}$ , and exhibits natural frequencies at 2.8 Hz and 7.6 Hz. The radiation patterns for a reflectarray formed from the structure are calculated using a ray-tracing approach for the nominal and mechanically deformed cases. The mechanically deformed antenna pattern is defocused, and a SAR system model shows this reduces the signal-to-noise ratio of a point scattering target by approximately 6 dB.

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Energy-Efficient Data Offloading for Earth Observation Satellite Networks

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arXiv 2024年

作者： He, Lijun Jia, Ziye Wang, Juncheng Wang, Feng Lansard, Erick Yuen, Chau School of Software Northwestern Polytechnical University China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China Department of Computer Science Hong Kong Baptist University China Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

In Earth Observation Satellite Networks (EOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving the data offloading efficiency. As such, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in EOSNs, aiming to balance the objectives of reducing the total energy consumption and increasing the sum weights of tasks. First, we derive the optimal power allocation solution to the joint optimization problem when the task scheduling policy is given. Second, leveraging the conflict graph model, we transform the original joint optimization problem into a maximum weight independent set problem when the power allocation strategy is given. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the sum weights of tasks and the total energy consumption, achieving superior system performance over the current best alternatives. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

A Comprehensive Review of Active EV Battery Cell Voltage Balancing systems: Current Issues and Prospective Solutions

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A Comprehensive Review of Active EV Battery Cell Voltage Bal...

On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Chandan Chetri Alvin Huynh Sheldon S. Williamson Department of Electrical Computer and Software Engineering Smart Transportation Electrification and Energy Research (STEER) Group Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Faculty of Engineering and Applied Science Ontario Tech University Oshawa Canada

The performance of a battery pack is greatly affected by an imbalance between the cells. Cell balancing is a very important criterion for Battery Management System (BMS) to operate properly. This paper presents the various cell balancing topologies which have been presented by various researchers in the past years. A novel topology has also been discussed, which improves the overall efficiency of the components being used. Various comparison parameters such as Control, Speed, Efficiency, Power Effects, Manufacturability, Size, and Cost have been considered.

关键词： Industrial electronics Electric potential Costs Equalizers Battery management systems Transformers Power electronics

Towards Blockchain-Based Secure Data Management for Remote Patient Monitoring

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arXiv 2022年

作者： Faruk, Md Jobair Hossain Shahriar, Hossain Valero, Maria Sneha, Sweta Ahamed, Sheikh I. Rahman, Mohammad Software Engineering Department Kennesaw State University United States Information Technology Department Kennesaw State University United States Information Systems Department Kennesaw State University United States Computer Science Department Marquette University United States Electrical and Computer Engineering Department Florida International University United States

Traditional data collection, storage and processing of Electronic Health Records (EHR) utilize centralized techniques that pose several risks of single point of failure and lean the systems to a number of internal and external data breaches that compromise their reliability and availability. Blockchain is an emerging distributed technology that can solve these issues due to its immutability and architectural nature that prevent records manipulation or alterations. In this paper, we discuss the progress and opportunities of remote patient monitoring using futuristic blockchain technologies and its two primary frameworks: Ethereum and Hyperledger Fabric. We also discuss the possible blockchain use cases in software engineering for systematic, disciplined, and quantifiable application development. The study extends by introducing a system architecture for EHR data management using Ethereum as a model. We discuss the challenges and limitations along with the initial evaluation results of the proposed system and draw future research directions in this promising area. © 2022, CC BY.

关键词： Blockchain

Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

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arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

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arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)