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Multi-user reinforcement learning based task migration in mobile edge computing

Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUI Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of TechnologyTianjin 300384China School of Internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

Mobile Edge computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

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Multi-Controller Architecture Design and Features in Software - Defined Networking (SDN) 6

Multi-Controller Architecture Design and Features in Softwar...

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6th International Conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prabhu, V. Balamurugan, P. Department of Computer Science and Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu603 203 India

ISBN: (纸本)9798331522667

Networking paradigm known as "Software-Defined Networking" (SDN) offers more flexibility with network management and is fast gaining popularity. Separating the control plane from the data plane is largely responsible for this success. In SDN-enabled networks, a centralized controller is used to translate high-level network application requirements into low-level directives that are issued to the forwarding devices. Such the controller requires limited ability to react to the request for flow processing generated by forwarding devices. Whenever an SDN-enabled network expands in terms of size or as a network traffic rises, flow processing demands can’t be able to fulfill by a single centralized controller. This causes a controller's performance to decline, which eventually results in a malfunctioning SDN-enabled network. Implementing a multi-controller architecture, in which a number of controllers cooperate to manage a sizable amount of network traffic and flow processing requirements, would be a successful strategy. The introduction of a multi-controller architecture and a consideration of the problems with its scalability, coherence as well as accessibility make up the core contribution of this study. Additionally, it identifies areas where study in this area could still be done. © 2025 IEEE.

关键词： Scalability

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CALTM:A Context-Aware Long-Term Time-Series Forecasting Model

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Computer Modeling in engineering & Sciences 2024年第4期139卷 873-891页

作者： Canghong Jin Jiapeng Chen Shuyu Wu Hao Wu Shuoping Wang Jing Ying School of Computer and Computing Science Hangzhou City UniversityHangzhou310015China School of Computer Science and Engineering Macao University of Science and TechnologyMacao999078China College of Computer Science and Technology Zhejiang UniversityHangzhou310058China

Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.

关键词： Traffic volume forecasting scene matching multi module fusion

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Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

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Multimedia Tools and Applications 2024年第18期84卷 1-22页

作者： Priyadharshini, A. Nithiya, S. Archana, H.A. Jaganathan, Subash Chandra Bose School of Engineering and Technology Bangaluru India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Agronomy SRM College of Agricultural Science Chengalpattu603201 India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

This research aims to integrate IoT with blockchain technology to securely manage and monitor sensitive patient health data in critical care environments, thereby improving the reliability and efficiency of patient monitoring systems. A hybrid blockchain architecture is employed to store ICU patient data securely, linking each patient's information with their respective doctors' details. This system operates without third-party intermediaries, ensuring trusted communication channels between patients and doctors. The blockchain ensures authorized access and secure transmission of medical histories and treatment plans, even allowing for remote care by doctors outside hospital premises. The integration of blockchain technology with IoT devices facilitates rapid and secure data transmission, enhancing the speed and accuracy of critical care delivery. The decentralized nature of blockchain ensures data integrity and prevents unauthorized access, while the hybrid approach allows for efficient and trusted collaboration among healthcare professionals. By harnessing the power of hybrid blockchain technology, this system not only safeguards sensitive medical information but also improves the overall quality of critical care through timely and informed medical intervention. The proposed solution holds the promise of reducing patient mortality rates by ensuring secure, efficient, and collaborative healthcare delivery. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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On learning the right attention point for feature enhancement

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Science China(Information Sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 1-14页

作者： Tan, Mingkui Chen, Guohao Wu, Jiaxiang Zhang, Yifan Chen, Yaofo Zhao, Peilin Niu, Shuaicheng South China University of Technology School of Software Engineering China Pazhou Laboratory Guangzhou China XVERSE China National University of Singapore School of Computing Singapore Tencent AI Lab China Nanyang Technological University College of Computing and Data Science Singapore

Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particularly important when the test environment changes frequently. Although some recent attempts have been made to handle this task, we still face two key challenges: 1) prior methods have to perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications;2) while existing TTA solutions can significantly improve the test performance on out-of-distribution data, they often suffer from severe performance degradation on in-distribution data after TTA (known as catastrophic forgetting). To this end, we have proposed an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples for test-time entropy minimization. To alleviate forgetting, EATA introduces a Fisher regularizer estimated from test samples to constrain important model parameters from drastic changes. However, in EATA, the adopted entropy loss consistently assigns higher confidence to predictions even when the samples are underlying uncertain, leading to overconfident predictions that underestimate the data uncertainty. To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA. Specifically, we compare the divergence between predictions from the full network and its sub-networks to measure the reducible model uncertainty, on which we propose a test-time uncertainty reduction strategy with divergence minimization loss to encourage consistent predictions instead of overconfident ones. To further re-calibrate predicting confidence on different samples, we utilize the disagreement among predicted labels as an indicator of the data uncertainty. Based on this, we devise a min-max entropy

关键词： Semantic Segmentation

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VisPhis: Automated Phishing Detection Bot for Open Source VoIP Applications 5

VisPhis: Automated Phishing Detection Bot for Open Source Vo...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Shaik, Nasir Hussain Kumar, Neelesh Menon, Rudraksh Sujatha, G. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur India

ISBN: (纸本)9798331518097

Mobile and Commercial devices are at high risk when navigating through websites and clicking on various links. Due to lack of knowledge, Individuals click on various links showing up on legitimate chats leading to clickjacking and phishing attacks. Every time a link shows up, Users cannot go to a site like virustotal and scan and wait for them to detect. This project aims to implement a structured script design and an automated program that scans and flags links as malicious and prevents certain cyberattacks. © 2024 IEEE.

关键词： Bot (Internet)

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Big data meets big wind:A scientometric review of machine learning approaches in offshore wind energy

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Energy and AI 2024年第4期18卷 1-12页

作者： Prangon Das Maisha Mashiata Gregorio Iglesias Department of Mechatronics Engineering Rajshahi University of Engineering&TechnologyRajshahi 6204Bangladesh School of Engineering&MaREI Environmental Research InstituteUniversity College CorkCollege Rd.CorkIreland School of Engineering Computing and MathematicsPlymouth UniversityDrake CircusPlymouthUK

Offshore wind energy offers several advantages relative to its onshore counterpart-not least stronger and steadier winds,the possibility of larger turbines,and no land *** operational complexities,environmental challenges,and higher maintenance costs of offshore wind turbines necessitate innovative *** approaches are insufficient,and new“big data”techniques,notably machine learning and deep learning,are poised to play a significant role in the design and optimisation of offshore wind turbines and *** objective of this paper is to conduct a scientometric analysis of machine learning and deep learning techniques applied to offshore wind *** research methodology employs a circular framework,integrating data acquisition and statistical analysis to provide a comprehensive scientometric insight into the state of the *** regards the country of origin,most of the publications stem from just five countries,which signals a need of greater geographical diversity in this field of *** importantly,the rapid,steady increase in the annual number of publications since 2017 reveals the interest of the research community in this novel topic.

关键词： Offshore wind Wind energy Machine learning Deep learning Neural network Artificial intelligence

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Toward a Framework to Improve Employees’ Compliance with Cybersecurity Policy in Organizations 8th

Toward a Framework to Improve Employees’ Compliance with Cy...

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Thamae, Reneuoe Abdullah, Hanifa Mujinga, Mathias School of Computing College of Science Engineering and Technology University of South Africa Pretoria South Africa

ISBN: (纸本)9789819930425

Digital transformation has influenced organizations’ operations significantly. However, non-compliance with cybersecurity policy (CSP) is a growing concern for organizations. technology alone cannot protect organizational cyber assets such as computer systems, networks, and data. Human aspects should be considered when designing and implementing a CSP. The lack of effective CSP training and awareness programs (CSPTAP) is attributable to employees’ non-compliance with the CSP. This paper aims to develop a framework to enhance employees’ compliance with the CSP by implementing effective CSPTAP. Drawing from the present literature and reflecting on the existing behavior change wheel (BCW) framework, and capability, opportunity, motivation, and behavior (COM-B) model, the cybersecurity policy compliance (CSPC) framework is developed. The CSPC framework comprises the following key concepts: learning existing policies, conducting employees’ gap analysis, reviewing existing policies/developing new policies, provision of relevant content and delivery mode, and periodic auditing. These key concepts are essential elements in cybersecurity policy compliance. The model indicates that the implementation of the essential elements will substantially influence employees’ compliance with CSP. Moreover, when organizations consider these key elements, cybersecurity policy training and awareness, can positively enhance employees’ CSP compliance. The proposed development of CSPTAP provides a firm base for future empirical work including action research. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Personnel training

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Enhancing Cybersecurity Policy Awareness Programs in the Financial Sector: A Comprehensive Framework and Assessment of Effectiveness 1

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International Conference on Business and technology, ICBT 2024

作者： Thamae, Reneuoe Abdullah, Hanifa Mujinga, Mathias School of Computing College of Science Engineering and Technology University of South Africa Pretoria South Africa

ISBN: (数字)9783031674440

ISBN: (纸本)9783031674433

In today's tech-driven landscape and amid rising cyber threats, prioritizing cybersecurity is crucial for financial organizations. While traditional measures like firewalls are insufficient, human vulnerability persists due to inadequate employee training. To solve this, institutions must enact comprehensive awareness programs on cybersecurity policies. Despite their recognized importance, research gaps exist regarding their effectiveness. This study proposes a framework to enhance such programs, drawing from literature reviews and expert insights. Using a quantitative approach, it examines employee perspectives, revealing gender disparities in cybersecurity knowledge. The framework integrates gender mainstreaming to address these gaps and recommends proactive measures such as inclusive training and reporting procedures. By emphasizing inclusivity and diversity, it aims to foster a robust cybersecurity culture and ensure policy adherence across financial organizations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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