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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Suja A. Alex Neha Singh Mainak Adhikari Department of Information Technology St. Xavier’s Catholic College of Engineering Nagercoil India Department of Computer Science Indian Institute of Information Technology Lucknow Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

the advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. the proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. the predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. this research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management.

关键词： Performance evaluation 6G mobile communication Training Analytical models Nearest neighbor methods Dynamic scheduling Mobile handsets Resource management Servers Time factors

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ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Context-Aware Data Protection

ZTAAC : Zero Trust Adaptive Authorization with CP-ABE for Co...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Kasturi Routray Padmalochan Bera Indian Institute of Technology Bhubaneswar India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In modern dynamic and distributed computing environments, secure and adaptive access control for sensitive data is essential. Traditional access control models often fail to address the complexities of evolving and heterogeneous systems. To overcome these challenges, we propose a Zero Trust Adaptive Authorization Architecture (ZTAAC) that integrates the principles of Zero Trust, Never trust, always verify, along with the cryptographic flexibility of a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme. ZTAAC introduces a central zero trust controller, which incorporates a policy decision point and a risk engine to facilitate real-time risk assessment and dynamic policy enforcement. the risk engine evaluates contextual parameters such as user location, device posture, and behaviour patterns to enable real-time adaptation of access policies through feedback loops that refine access decisions dynamically. Our enhanced CP-ABE framework embeds static and dynamic attributes-based access policies within encrypted data, ensuring that only authorized users with valid context can decrypt it. Dynamic attribute management facilitates the generation and integration of key components for contextual attributes and aligns access decisions with current security postures. Preliminary performance analysis confirms ZTAAC’s scalability and efficiency, establishing it as a robust and adaptive solution for securing data in next-generation cloud-assisted and distributed environments.

关键词： Authorization Feedback loop Scalability distributed databases Real-time systems Zero Trust Performance analysis Risk management Engines Next generation networking

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Dynamic Staleness Control for Asynchronous Federated Learning in Decentralized Topology 18th

Dynamic Staleness Control for Asynchronous Federated Learni...

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18th international conference on Wireless Artificial intelligent computing Systems and Applications, WASA 2024

作者： Ma, Qianpiao Liu, Jianchun Jia, Qingmin Zhou, Xiaomao Hu, Yujiao Xie, Renchao Purple Mountain Laboratories Jiangsu Nanjing China School of Computer Science and Technology University of Science and Technology of China Anhui Hefei China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783031714665

Decentralized federated learning (DFL) has emerged as a promising paradigm for distributed machine learning over edge nodes (i.e., workers) without relying on a centralized parameter server. Most existing DFL researches rely on synchronous communication among workers. However, due to edge heterogeneity and dynamic network conditions, synchronous DFL mechanisms may suffer from inefficient model training and poor scalability. Meanwhile, the existing asynchronous DFL (ADFL) mechanisms present the challenge of stale models among workers, leading to diminished training quality, especially on Non-IID data. In this paper, we propose a novel staleness-aware ADFL (SA-ADFL) mechanism, aiming to realize a trade-off between model training efficiency and quality by dynamic staleness control. Specifically, we provide rigorous theoretical proof for SA-ADFL and formulate the worker scheduling problem to minimize total model training time under flexible long-term staleness constraints. then we decompose the original round-coupled problem into a series of single-round sub-problems by leveraging the Lyapunov optimization, enabling efficient worker selection to minimize training time in each round while ensuring staleness queue stability. Experimental results demonstrate that our SA-ADFL accelerates model training by approximately 52.9% while maintaining equivalent model training accuracy compared with the state-of-the-art mechanisms. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Achieving time-critical control in Edge Robotics Systems over 5G

Achieving time-critical control in Edge Robotics Systems ove...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： Amit Baxi Vincent Mageshkumar Girish S Murthy Arvind Merwaday Arvind Raju Gowtham Hosamane Sugnan Prabhu Venkat Natarajan Susruth Sudhakaran Dave Cavalcanti Atul N Hatalkar Christian Maciocco Ravishankar Iyer Rath Vannithamby Intel Technology India Private Limited Bangalore India Intel Corporation Hillsboro OR USA

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

In this paper, we present the challenges and solutions to achieve time-critical robot control in an Edge computing System, when the sensing-computing-control functions are distributed and connected over a 5G network. We describe these challenges/solutions in context of a real 5G Edge Robotics System testbed, where the task for a robot arm is to pick up moving objects from a conveyer belt but with vision perception and other AI computing workloads running remotely on an Edge computing Server, connected over 5G. We present a learning-based compute-communications-control Co-design approach which enables (a) robust robotic control over an unreliable wireless network and (b) dynamic QoS adaptation to reduce network traffic. In addition, we also highlight simulation-to-real challenges and strategies in deploying reinforcement learning policies trained in simulation to control a real robot arm. Finally, we present overall architecture, experimental results, and experiences in enabling a real-world 5G Edge Robotic system.

关键词： 5G mobile communication Wireless networks Robot control Quality of service Robot sensing systems Manipulators Sensors Time factors Servers Robots

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Attend, Distill, Detect: Attention-Aware Entropy Distillation for Anomaly Detection 27th

Attend, Distill, Detect: Attention-Aware Entropy Distillatio...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Jena, Sushovan Saini, Vishwas Shaw, Ujjwal Jain, Pavitra Raihal, Abhay Singh Banerjee, Anoushka Joshi, Sharad Ganesh, Ananth Bhavsar, Arnav School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh Mandi175005 India R&D Center Hitachi India Pvt. Ltd. Bengaluru560055 India

ISBN: (纸本)9783031781681

Unsupervised anomaly detection encompasses diverse applications in industrial settings where a high-throughput and precision is imperative. Early works were centered around one-class-one-model paradigm, which poses significant challenges in large-scale production environments. Knowledge-distillation based multi-class anomaly detection promises a low latency with a reasonably good performance but with a significant drop as compared to one-class version. We propose a DCAM (distributed Convolutional Attention Module) which improves the distillation process between teacher and student networks when there is a high variance among multiple classes or objects. Integrated multi-scale feature matching strategy to utilise a mixture of multi-level knowledge from the feature pyramid of the two networks, intuitively helping in detecting anomalies of varying sizes which is also an inherent problem in the multi-class scenario. Briefly, our DCAM module consists of Convolutional Attention blocks distributed across the feature maps of the student network, which essentially learns to masks the irrelevant information during student learning alleviating the "cross-class interference" problem. this process is accompanied by minimizing the relative entropy using KL-Divergence in Spatial dimension and a Channel-wise Cosine Similarity between the same feature maps of teacher and student. the losses contributes to achieve scale-invariance and capture non-linear relationships. We also highlight that the DCAM module would only be used during training and not during inference as we only need the learned feature maps and losses for anomaly scoring and hence, gaining a performance gain of 3.92% than the multi-class baseline with a preserved latency. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Scaling-Up Jenkins with a Single Node Instance in DevOps 12th

Scaling-Up Jenkins with a Single Node Instance in DevOps

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12th international conference on Frontiers of intelligent computing: theory and Applications, FICTA 2024

作者： Jagannadha Rao, D.B. Harinatha Reddy, Yanamala Polepally, Vijayakumar Malla Reddy University Hyderabad India Research Schalar Department of CSE Malla Reddy University Hyderabad India Kakatiya Institute of Technology &Science Warangal India

ISBN: (纸本)9789819601424

DevOps is a set of procedures and instruments that streamlines and automates IT operations (Ops) and software development (Dev) tasks in order to accelerate and enhance the systems development life cycle. the goal of DevOps is to enhance work at every stage of the software development process. Formerly divided responsibilities including development, IT operations, quality engineering, and security may now coordinate and work together thanks to DevOps Jenkins is a leading open-source automation server, Jenkins provides hundreds of plugins to support building, deploying and automating any project. Jenkins can be used as a simple Continuous integration server or turned into the continuous delivery hub for any project. the traditional way of scaling-up Jenkins is by building a distributed network across multiple machines, helping drive builds, tests, and deployments across multiple platforms faster. However here, we are trying to explore the options and define the scope to scale-up without incurring additional cost by avoiding procurement of additional hardware and spending less time on building the solution for the organizational project needs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Life cycle

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Hybrid Sine-Cosine Chimp Optimization of Adaptive distributed Denial-of-Service Detection and Classification for Blockchain 12th

Hybrid Sine-Cosine Chimp Optimization of Adaptive Distribute...

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12th international conference on Frontiers of intelligent computing: theory and Applications, FICTA 2024

作者： Mrunalini, G. Vijaya Babu, K. Kesava Reddy, V. Sudha Pavani, K. Ranjith, Jinugu Uday Kishore, G. CMR Engineering College Telangana Hyderabad India Department of Computer Science and Engineering CMR Engineering College Telangana Hyderabad India Department of Humanities & Science CMR Technical Campus Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

Because of its decentralized, transparent, and secure characteristics, blockchain technology is becoming more and more popular. therefore, it is imperative to make sure that it is resilient to network threat, particularly distributed denial-of-service (DDoS) assaults. this study tackles the susceptibility of the blockchain system to denial-of-service attacks (DDoS) that compromise its fundamental decentralized features and jeopardize their dependability and security. We have developed a new adaptive integration method for identifying and detecting different types of DDoS attacks. In order to verify the resilience and accuracy of our methodology, we extracted a dataset that combined several DDoS attacks. this allowed us to use our methodology to identify DDoS threats and then divide them into seven distinct assault subcategories. A comprehensive framework that smoothly integrates a hybrid sine–cosine chimp optimization model has been suggested to address the wide range of variances in DDoS attacks. Our framework’s novel feature is addition of the dynamic weight adjustment method, which improves system flexibility. Empirical findings validate the effectiveness of our ensemble approach over singular models for a range of assessment criteria. With rates for classification tasks and 99.56% for detection, the framework demonstrated impressive accuracy. this work opens the door for enhancing blockchain systems’ security mechanisms against DDoS attacks by creating an all-encompassing and flexible methodology. the dynamic weight adjustment in conjunction with the ensemble technique shows promise in guaranteeing the long-term security and reliability of blockchain technology. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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A Meta-reinforcement Learning Framework for Adaptive Quadrotor UAV Attitude Control 25th

A Meta-reinforcement Learning Framework for Adaptive Quadro...

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25th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Zhao, Kaidong Li, Yanjie Liu, Zihan Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China School of Mechanical Engineering and Automation The Harbin Institute of Technology Shenzhen Shenzhen518055 China

ISBN: (纸本)9789819642069

this paper presents a meta-reinforcement learning approach for quadrotor UAV attitude control, addressing the limitations of traditional control methods in complex, dynamic, and uncertain environments. Our method integrates the advantages of context learning, Gaussian sampling, and multi-task training to rapidly adapt to diverse aircraft parameters and environmental conditions. Specifically, we utilize historical information quadruples as contextual data to accurately estimate current task characteristics through real-time observation and learning. In the latent space, we employ Gaussian sampling to effectively handle unknown UAV parameters. To validate the method’s efficacy, we conducted a series of experiments in the Bullet simulator, including single attitude tracking, resultant acceleration control, and continuous trajectory tracking. Experimental results demonstrate that our approach significantly outperforms traditional PID controllers and domain randomization-based SAC methods in terms of stability, rapid response, and trajectory tracking precision. through multi-task training, our controller has developed the ability to swiftly adapt to novel situations. this research provides a robust and flexible solution for adaptive control of quadrotor UAVs, showing promise for more efficient and stable flight control in complex and dynamically changing environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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distributed Event-Triggering Secondary Frequency Control of Islanded Microgrids Under DoS Attacks 8th

Distributed Event-Triggering Secondary Frequency Control of...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhou, Lihua Zhang, Changda Du, Dajun Fei, Minrui Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Electrical Engineering Hebei University of Science and Technology Hebei China

ISBN: (纸本)9789819602247

High reliability and efficiency of microgrids are being threatened by cyber attacks. this paper investigates a Secondary Frequency Control Scheme (SFCS) for an islanded microgrid with Event-Triggering Mechanism (ETM) under Denial of Service (DoS) attacks. Firstly, the microgrid dynamic model is reviewed and control objectives are formulated. Secondly, a cooperative frequency control scheme combined with distributed event-triggering mechanism is presented to achieve the desired frequency control under DoS attacks. thirdly, the stability and Zeno Behaviour of the closed-loop system are analysed by Lyapunov theory. Finally, simulation results of the microgrid are explored to verify the effectiveness of the proposed frequency control scheme. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Intrusion Detection at the Edge computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection Real-time systems Internet of things Security Servers Long short term memory Edge computing

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